@inproceedings{Pastorelli,
  volume = { 2015-Novem },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971310302{\&}doi=10.1109{\%}2FBEC.2014.7320591{\&}partnerID=40{\&}md5=3476efbc0430ba44724fcc8006637b70 },
  type = { Conference Proceedings },
  title = { Virtual Reality visualization for short fibre orientation analysis },
  pages = { 201--204 },
  doi = { 10.1109/BEC.2014.7320591 },
  booktitle = { Proceedings of the Biennial Baltic Electronics Conference, BEC },
  author = { Pastorelli and Herrmann },
  year = { To Appear },
}

@inproceedings{Schiwarth,
  volume = { 406 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054221856{\&}doi=10.1088{\%}2F1757-899X{\%}2F406{\%}2F1{\%}2F012014{\&}partnerID=40{\&}md5=e1757458a87eb396d4743ad19f658d5a },
  type = { Conference Proceedings },
  title = { Visual analysis of void and reinforcement characteristics in X-ray computed tomography dataset series of fiber-reinforced polymers },
  doi = { 10.1088/1757-899X/406/1/012014 },
  booktitle = { IOP Conference Series: Materials Science and Engineering },
  author = { Schiwarth and Weissenb{\"{o}}ck and Plank and Fr{\"{o}}hler and Heinzl and Kastner },
  year = { To Appear },
}

@inproceedings{Ponzio,
  volume = { 2017-Janua },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049240676{\&}partnerID=40{\&}md5=0189c956d91dec04b477ed5846ecb930 },
  type = { Conference Proceedings },
  title = { A multi-modal brain image registration framework for US-guided neuronavigation systems integrating MR and US for minimally invasive neuroimaging },
  pages = { 114--121 },
  booktitle = { BIOIMAGING 2017 - 4th International Conference on Bioimaging, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017 },
  author = { Ponzio and Macii and Ficarra and {Di Cataldo} },
  year = { To Appear },
}

@inproceedings{Suter,
  volume = { 2018-April },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048099086{\&}doi=10.1109{\%}2FISBI.2018.8363752{\&}partnerID=40{\&}md5=e22ed68e255aa6c6af3b32100df7276a },
  type = { Conference Proceedings },
  title = { Fast and uncertainty-aware cerebral cortex morphometry estimation using random forest regression },
  pages = { 1052--1055 },
  doi = { 10.1109/ISBI.2018.8363752 },
  booktitle = { Proceedings - International Symposium on Biomedical Imaging },
  author = { Suter and Rummel and Wiest and Reyes },
  year = { To Appear },
}

@inproceedings{Mandl,
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874185461{\&}partnerID=40{\&}md5=a9627e50e42db43cbfa2f5ca618bdad4 },
  type = { Conference Proceedings },
  title = { Towards a numerical 3D model of functional electrical stimulation of denervated, degenerated human skeletal muscle },
  booktitle = { 21st European Modeling and Simulation Symposium, EMSS 2009 },
  author = { Mandl and Martinek and Mayr and Rattay and Reichel and Moser },
  year = { To Appear },
}

@article{Ceranka2020,
  year = { 2020 },
  volume = { 83 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multi-atlas segmentation of the skeleton from whole-body MRI—Impact of iterative background masking },
  pages = { 1851--1862 },
  number = { 5 },
  keywords = { atlas-based segmentation,bone segmentation,image registration,whole-body MRI },
  journal = { Magnetic Resonance in Medicine },
  issn = { 15222594 },
  doi = { 10.1002/mrm.28042 },
  author = { Ceranka and Verga and Kvasnytsia and Lecouvet and Michoux and Mey and Raeymaekers and Metens and Absil and Vandemeulebroucke },
  abstract = { Purpose: To improve multi-atlas segmentation of the skeleton from whole-body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation. Methods: An improved atlas registration scheme is proposed. Starting from a suitable initial alignment, the alignment is refined by introducing additional stages of deformable registration during which the image sampling is limited to the dilated atlas segmentation label mask. The performance of the method was demonstrated using leave-one-out cross-validation using atlases of 10 whole-body 3D-T1 images of prostate cancer patients with bone metastases and healthy male volunteers, and compared to existing state of the art. Both registration accuracy and resulting segmentation quality, using four commonly used label fusion strategies, were evaluated. Results: The proposed method showed significant improvement in registration and segmentation accuracy with respect to the state of the art for all validation criteria and label fusion strategies, resulting in a Dice coefficient of 0.887 (STEPS label fusion). The average Dice coefficient for the multi-atlas segmentation showed over 11{\%} improvement with a decrease of false positive rate from 28.3{\%} to 13.2{\%}. For this application, repeated application of the background masking did not lead to significant improvement of the segmentation result. Conclusions: A registration strategy, relying on the use of atlas segmentations as mask during image registration was proposed and evaluated for multi-atlas segmentation of whole-body MRI. The approach significantly improved registration and final segmentation accuracy and may be applicable to other structures of interest. },
}

@article{Song2020,
  year = { 2020 },
  volume = { 103 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074942779{\&}doi=10.1016{\%}2Fj.jmbbm.2019.103533{\&}partnerID=40{\&}md5=5a9d1f1c5ade6a354c2e6bf7adb9613f },
  type = { Journal Article },
  title = { Enhancement of mechanical strength of TCP-alginate based bioprinted constructs },
  keywords = { Alginate-TCP bioink,Bioprinting,Bone defect,PCL supporter,Unit-assembly model },
  journal = { Journal of the Mechanical Behavior of Biomedical Materials },
  issn = { 18780180 },
  doi = { 10.1016/j.jmbbm.2019.103533 },
  author = { Song and Fu and Raza and Shen and Xue and Wang and Wang },
  abstract = { To overcome the mechanical drawback of bioink, we proposed a supporter model to enhance the mechanical strength of bioprinted 3D constructs, in which a unit-assembly idea was involved. Based on Computed Tomography images of critical-sized rabbit bone defect, the 3D re-construction was accomplished by a sequenced process using Mimics 17.0, BioCAM and BioCAD software. 3D constructs were bioprinted using polycaprolactone (PCL) ink for the outer supporter under extrusion mode, and cell-laden tricalcium phosphate (TCP)/alginate bioink for the inner filler under air pressure dispensing mode. The relationship of viscosity of bioinks, 3D bioprinting pressure, TCP/alginate ratio and cell survival were investigated by the shear viscosities analysis, live/dead cell test and cell-counting kit 8 measurement. The viscosity of bioinks at 1.0 s−1-shear rate could be adjusted within the range of 1.75 ± 0.29 Pa{\textperiodcentered}s to 155.65 ± 10.86 Pa{\textperiodcentered}s by changing alginate concentration, corresponding to 10 kPa–130 kPa of printing pressure. This design with PCL supporter could significantly enhance the compressive strength and compressive modulus of standardized 3D mechanical testing specimens up to 2.15 ± 0.14 MPa to 2.58 ± 0.09 MPa, and 42.83 ± 4.75 MPa to 53.12 ± 1.19 MPa, respectively. Cells could maintain the high viability (over 80{\%}) under the given printing pressure but cell viability declined with the increase of TCP content. Cell survival after experiencing 7 days of cell culture could be achieved when the ratio of TCP/alginate was 1 : 4. All data supported the feasibility of the supporter and unit-assembly model to enhance mechanical properties of bioprinted 3D constructs. },
}

@article{Montin2020,
  year = { 2020 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079384742{\&}doi=10.1007{\%}2Fs11517-019-02109-4{\&}partnerID=40{\&}md5=211d8231bb9224f67092a15ccbb09ce3 },
  type = { Journal Article },
  title = { A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology },
  keywords = { Brain MRI,Deformable registration,Image registration,Mutual information,Normalized gradient field,Pediatric brain tumors },
  journal = { Medical and Biological Engineering and Computing },
  issn = { 17410444 },
  doi = { 10.1007/s11517-019-02109-4 },
  author = { Montin and Belfatto and Bologna and Meroni and Cavatorta and Pecori and Diletto and Massimino and Oprandi and Poggi and Arrigoni and Peruzzo and Pignoli and Gandola and Cerveri and Mainardi },
  abstract = { Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric approaches (Mplus, Mdot), combining mutual information (MI) and normalized gradient field filter (NGF). The registration performance of the proposed metrics was assessed on a simulated dataset (Brainweb) and compared with those obtained by MI and NGF separately, using mean magnitude and mean angular errors. The most promising metric (Mplus) was then selected and tested on a retrospective dataset comprising 45 pediatric patients who underwent focal radiotherapy for brain cancer. The quality of the realignment was scored by a radiation oncologist using a perceived misalignment metric (PM). All patients but one were assessed as PM ≤ 2 (good alignment), but the remaining one, severely affected by hydrocephalus and pneumocephalus at the first MRI acquisition, scored PM = 5 (unacceptable). These preliminary findings suggest that Mplus might improve the registration accuracy in complex applications such as pediatric oncology, when data are acquired throughout the years of follow-up, and is worth investigating. [Figure not available: see fulltext.]. },
}

@article{Gerst2020,
  year = { 2020 },
  volume = { 11 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078663806{\&}doi=10.1016{\%}2Fj.softx.2020.100405{\&}partnerID=40{\&}md5=4063263c3ae932dded430bc8192b1704 },
  type = { Journal Article },
  title = { MISA++: A standardized interface for automated bioimage analysis },
  keywords = { Application integration,Big volume image data,Image processing,Light-sheet fluorescence microscopy,Parallelization },
  journal = { SoftwareX },
  issn = { 23527110 },
  doi = { 10.1016/j.softx.2020.100405 },
  author = { Gerst and Medyukhina and Figge },
  abstract = { Modern imaging techniques, such as lightsheet fluorescence microscopy (LSFM), allow the capture of whole organs in three spatial dimensions. The analysis of these big volume image data requires a combination of user-friendly and highly efficient tools. We here present MISA++, an image analysis framework that allows easy integration of custom high-performance C++ tools into third-party applications via standardized components for parallelization, data and parameter handling, command line interface, and communication with third-party applications. We demonstrate its capabilities by implementing a plugin for ImageJ that provides a graphical user interface for any application built with our framework, and a high-performance re-implementation of our Python-based algorithm to segment glomeruli in LSFM images of whole murine kidneys. },
}

@article{RN839,
  year = { 2020 },
  volume = { 15 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074526687{\&}doi=10.1007{\%}2Fs11548-019-02071-4{\&}partnerID=40{\&}md5=d65f50a05a3b5b16c3d481c565ea1497 },
  type = { Journal Article },
  title = { Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences },
  pages = { 119--128 },
  number = { 1 },
  keywords = { Echocardiography,Mitral valve,Position-based dynamics,Segmentation,Tracking },
  journal = { International Journal of Computer Assisted Radiology and Surgery },
  issn = { 18616429 },
  doi = { 10.1007/s11548-019-02071-4 },
  author = { Tautz and Walczak and Georgii and Jazaerli and Vellguth and Wamala and S{\"{u}}ndermann and Falk and Hennemuth },
  abstract = { Purpose: For planning and guidance of minimally invasive mitral valve repair procedures, 3D+t transesophageal echocardiography (TEE) sequences are acquired before and after the intervention. The valve is then visually and quantitatively assessed in selected phases. To enable a quantitative assessment of valve geometry and pathological properties in all heart phases, as well as the changes achieved through surgery, we aim to provide a new 4D segmentation method. Methods: We propose a tracking-based approach combining gradient vector flow (GVF) and position-based dynamics (PBD). An open-state surface model of the valve is propagated through time to the closed state, attracted by the GVF field of the leaflet area. The PBD method ensures topological consistency during deformation. For evaluation, one expert in cardiac surgery annotated the closed-state leaflets in 10 TEE sequences of patients with normal and abnormal mitral valves, and defined the corresponding open-state models. Results: The average point-to-surface distance between the manual annotations and the final tracked model was 1.00mm±1.08mm. Qualitatively, four cases were satisfactory, five passable and one unsatisfactory. Each sequence could be segmented in 2–6 min. Conclusion: Our approach enables to segment the mitral valve in 4D TEE image data with normal and pathological valve closing behavior. With this method, in addition to the quantification of the remaining orifice area, shape and dimensions of the coaptation zone can be analyzed and considered for planning and surgical result assessment. },
}

@article{RN806,
  year = { 2020 },
  volume = { 101 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Patient specific characterization of artery and plaque material properties in peripheral artery disease },
  pages = { 14 },
  keywords = { Intravascular ultrasound,Inverse finite element analysis,Peripheral artery disease,Pressure inflation testing,Virtual histology },
  journal = { Journal of the Mechanical Behavior of Biomedical Materials },
  issn = { 18780180 },
  doi = { 10.1016/j.jmbbm.2019.103453 },
  author = { Noble and Carlson and Neumann and Dragomir-Daescu and Erdemir and Lerman and Young },
  abstract = { Patient-specific finite element (FE) modeling of atherosclerotic plaque is challenging, as there is limited information available clinically to characterize plaque components. This study proposes that for the limited data available in vivo, material properties of plaque and artery can be identified using inverse FE analysis and either a simple neo-Hookean constitutive model or assuming linear elasticity provides sufficient accuracy to capture the changes in vessel deformation, which is the available clinical metric. To test this, 10 human cadaveric femoral arteries were each pressurized ex vivo at 6 pressure levels, while intravascular ultrasound (IVUS) and virtual histology (VH) imaging were performed during controlled pull-back to determine vessel geometry and plaque structure. The VH images were then utilized to construct FE models with heterogeneous material properties corresponding to the vessel plaque components. The constitutive models were then fit to each plaque component by minimizing the difference between the experimental and the simulated geometry using the inverse FE method. Additionally, we further simplified the analysis by assuming the vessel wall had a homogeneous structure, i.e. lumping artery and plaque as one tissue. We found that for the heterogeneous wall structure, the simulated and experimental vessel geometries compared well when the fitted neo-Hookean parameters or elastic modulus, in the case of linear elasticity, were utilized. Furthermore, taking the median of these fitted parameters then inputting these as plaque component mechanical properties in the finite element simulation yielded differences between simulated and experimental geometries that were on average around 2{\%} greater (1.30–5.55{\%} error range to 2.33–11.71{\%} error range). For the homogeneous wall structure the simulated and experimental wall geometries had an average difference of around 4{\%} although when the difference was calculated using the median fitted value this difference was larger than for the heterogeneous fits. Finally, comparison to uniaxial tension data and to literature constitutive models also gave confidence to the suitability of this simplified approach for patient-specific arterial simulation based on data that may be acquired in the clinic. },
}

@article{RN829,
  year = { 2020 },
  volume = { 71 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079849262{\&}doi=10.1016{\%}2Fj.ejmp.2020.02.016{\&}partnerID=40{\&}md5=ffe26ce9da5145f82cc70d5a9518fab4 },
  type = { Journal Article },
  title = { A quantitative evaluation of deformable image registration based on MV cone beam CT images: Impact of deformation magnitudes and image modalities },
  pages = { 82--87 },
  keywords = { Adaptive radiotherapy,Deformable image registration,MVCBCT },
  journal = { Physica Medica },
  issn = { 1724191X },
  doi = { 10.1016/j.ejmp.2020.02.016 },
  author = { Huang and Li and Wang and Hu and Wang and Chang and Ma and Li and Wu and Zhang },
  abstract = { Background and purpose: To evaluate the impact of deformation magnitude and image modality on deformable-image-registration (DIR) accuracy using Halcyon megavoltage cone beam CT images (MVCBCT). Materials and methods: Planning CT images of an anthropomorphic Head phantom were aligned rigidly with MVCBCT and re-sampled to achieve the same resolution, denoted as pCT. MVCBCT was warped with twenty simulated pre-known virtual deformation fields (Ti, i = 1–20) with increasing deformation magnitudes, yielding warped CBCT (wCBCT). The pCT and MVCBCT were registered to wCBCT respectively (Multi-modality and Uni-modality DIR), generating deformation vector fields Vi and Vi′ (i = 1–20). Vi and Vi′ were compared with Ti respectively to assess the DIR accuracy geometrically. In addition, Vi, Ti, and Vi′ were applied to pCT, generating deformed CT (dCTi), ground-truth CT (Gi) and deformed CT′ (dCTi′) respectively. The Hounsfield Unit (HU) on these virtual CT images were also compared. Results: The mean errors of vector displacement increased with the deformation magnitude. For deformation magnitudes between 2.82 mm and 7.71 mm, the errors of uni-modality DIR were 1.16 mm {\~{}} 1.73 mm smaller than that of multi-modality (p = 0.0001, Wilcoxon signed rank test). DIR could reduce the maximum signed and absolute HU deviations from 70.8 HU to 11.4 HU and 208 HU to 46.2 HU respectively. Conclusions: As deformation magnitude increases, DIR accuracy continues to deteriorate and uni-modality DIR consistently outperformed multi-modality DIR. DIR-based adaptive radiotherapy utilizing the noisy MVCBCT images is only conditionally applicable with caution. },
}

@article{RN830,
  year = { 2020 },
  volume = { 20 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077786257{\&}doi=10.1186{\%}2Fs40644-020-0286-5{\&}partnerID=40{\&}md5=c2b44a405ae7ba7bfa48478311ee70a6 },
  type = { Journal Article },
  title = { Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI },
  number = { 1 },
  keywords = { Atlas-based segmentation,Diffusion weighted imaging,Multiple myeloma,Semi-automatic segmentation,Total lesion burden },
  journal = { Cancer Imaging },
  issn = { 14707330 },
  doi = { 10.1186/s40644-020-0286-5 },
  author = { Almeida and Santinha and Oliveira and Ip and Lisitskaya and Louren{\c{c}}o and Uysal and Matos and Joa{\~{o}} and Papanikolaou },
  abstract = { Background: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. Methods: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. Results: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p {\textless} 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. Conclusions: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment. },
}

@article{Ziegler2019a,
  year = { 2019 },
  volume = { 46 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom? },
  pages = { 3371--3377 },
  number = { 8 },
  keywords = { 4D dose calculation,dynamic tumor tracking,liver cancer,lung cancer },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.13654 },
  author = { Ziegler and Lettmaier and Fietkau and Bert },
  abstract = { Purpose: With the introduction of dynamic tumor tracking in radiotherapy, it is possible to irradiate moving targets with minimal safety margins. However, most dynamic tumor tracking techniques rely on changing the beam geometry by, for example, adapting the multileaf collimator (MLC) positions or rotating the LINAC head. These changes are relative to a reference position which is determined by a specific breathing phase. Since these changes in the beam path also influence the delivered dose, choosing a different reference position based on a different breathing phase impacts the applied dose to the patient. This work investigates the influence of choosing different reference breathing phases on the dose distribution. Methods: The Vero system tracks the moving target by performing a pan and tilt rotation of the LINAC head. For 13 patients, the target position was extracted from every phase of a four-dimensional computed tomography (4DCT) and the pan and tilt values were determined with respect to three different reference phases. These reference phases were inspiration, expiration, and the midventilation. For all reference phases, a 4D dose calculation was performed on the 4DCT regarding the respective pan and tilt values. Furthermore, the applied dose to the target and surrounding organs at risk was calculated. To accumulate the dose distribution, weights from the actual patient breathing motion were determined. The weights were calculated from the breathing motions from different days to investigate the impact of daily variations in the breathing motion onto the accumulated dose distribution. All obtained values were then compared to the static treatment plan. Results: The mean and maximum doses applied to the target or surrounding organs at risk show no general behavior depending on the different reference phases. Nevertheless, for some patients, large differences (approx. 30{\%}) in the applied dose to certain organs at risk could be observed, whereas the applied dose to the target shows no dependency on the different reference phases. However, the mean target dose is in all cases approx. 1.5{\%} below the reference value from the static treatment plan. Conclusion: Although no general dependency of the applied dose on the selected reference phase could be found, the choice of the reference phase can have great impact on the organ at risk dose for some patients. Thus, the choice of the reference phase used for patient positioning should be considered during treatment planning since it can be seen as a new degree of freedom of a treatment based on tracking. },
}

@article{Zhang2019,
  year = { 2019 },
  volume = { 46 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Interpolated CT for attenuation correction on respiratory gating cardiac SPECT/CT — A simulation study },
  pages = { 2621--2628 },
  number = { 6 },
  keywords = { attenuation correction,cardiac SPECT/CT,respiratory gating },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.13513 },
  author = { Zhang and Ghaly and Mok },
  abstract = { Purpose: Respiratory gated four-dimensional (4D) single photon emission computed tomography (SPECT) with phase-matched CT reduces respiratory blurring and attenuation correction (AC) artifacts in cardiac SPECT. This study aims to develop and investigate the effectiveness of an interpolated CT (ICT) method for improved cardiac SPECT AC using simulations. Methods: We used the 4D XCAT phantom to simulate a population of ten patients varied in gender, anatomy, 99mTc-sestamibi distribution, respiratory patterns, and disease states. Simulated 120 SPECT projection data were rebinned into six equal count gates. Activity and attenuation maps in each gate were averaged as gated SPECT and CT (GCT). Three helical CTs were simulated at end-inspiration (HCT-IN), end-expiration (HCT-EX), and mid-respiration (HCT-MID). The ICTs were obtained from HCT-EX and HCT-IN using the motion vector field generated between them from affine plus b-spline registration. Projections were reconstructed by OS-EM method, using GCT, ICT, and three HCTs for AC. Reconstructed images of each gate were registered to end-expiration and averaged to generate the polar plots. Relative difference for each segment and relative defect size were computed using images of GCT AC as reference. Results: The average of maximum relative difference through ten phantoms was 7.93 ± 4.71{\%}, 2.50 ± 0.98{\%}, 3.58 ± 0.74{\%}, and 2.14 ± 0.56{\%} for noisy HCT-IN, HCT-MID, HCT-EX, and ICT AC data, respectively. The ICT showed closest defect size to GCT while the differences from HCTs can be over 40{\%}. Conclusion: We conclude that the performance of ICT is similar to GCT. It improves the image quality and quantitative accuracy for respiratory-gated cardiac SPECT as compared to conventional HCT, while it can potentially further reduce the radiation dose of GCT. },
}

@article{Uthoff2019,
  year = { 2019 },
  volume = { 46 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors },
  pages = { 179--185 },
  number = { 3 },
  keywords = { Magnetic resonance imaging,Malignant peripheral nerve sheath tumor,Plexiform neurofibroma,Positron emission tomography,Quantitative feature extraction },
  journal = { Journal of Neuroradiology },
  issn = { 17730406 },
  doi = { 10.1016/j.neurad.2018.05.006 },
  author = { Uthoff and {De Stefano} and Panzer and Darbro and Sato and Khanna and Quelle and Meyerholz and Weimer and Sieren },
  abstract = { Background: This study explores whether objective, quantitative radiomic biomarkers derived from magnetic resonance (MR), positron emission tomography (PET), and computed tomography (CT) may be useful in reliably distinguishing malignant peripheral nerve sheath tumors (MPNST) from benign plexiform neurofibromas (PN). Methods: A registration and segmentation pipeline was established using a cohort of NF1 patients with histopathological diagnosis of PN or MPNST, and medical imaging of the PN including MR and PET-CT. The corrected MR datasets were registered to the corresponding PET-CT via landmark-based registration. PET standard-uptake value (SUV) thresholds were used to guide segmentation of volumes of interest: MPNST-associated PET-hot regions (SUV ≥ 3.5) and PN-associated PET-elevated regions (2.0 {\textless} SUV {\textless} 3.5). Quantitative imaging features were extracted from the MR, PET, and CT data and compared for statistical differences. Intensity histogram features included (mean, media, maximum, variance, full width at half maximum, entropy, kurtosis, and skewness), while image texture was quantified using Law's texture energy measures, grey-level co-occurrence matrices, and neighborhood grey-tone difference matrices. Results: For each of the 20 NF1 subjects, a total of 320 features were extracted from the image data. Feature reduction and statistical testing identified 9 independent radiomic biomarkers from the MR data (4 intensity and 5 texture) and 4 PET (2 intensity and 2 texture) were different between the PET-hot versus PET-elevated volumes of interest. Conclusions: Our data suggests imaging features can be used to distinguish malignancy in NF1-realted tumors, which could improve MPNST risk assessment and positively impact clinical management of NF1 patients. },
}

@article{Tournier2019,
  year = { 2019 },
  volume = { 202 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation },
  pages = { 17 },
  keywords = { Image,MRI,Processing,Software,Visualisation },
  journal = { NeuroImage },
  issn = { 10959572 },
  doi = { 10.1016/j.neuroimage.2019.116137 },
  author = { Tournier and Smith and Raffelt and Tabbara and Dhollander and Pietsch and Christiaens and Jeurissen and Yeh and Connelly },
  abstract = { MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software. },
}

@article{Taylor2019,
  year = { 2019 },
  volume = { 14 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Vascular effects on the BOLD response and the retinotopic mapping of hV4 },
  pages = { 32 },
  number = { 6 },
  journal = { PLoS ONE },
  issn = { 19326203 },
  doi = { 10.1371/journal.pone.0204388 },
  author = { {Boyd Taylor} and Puckett and Isherwood and Schira },
  abstract = { Despite general acceptance that the retinotopic organisation of human V4 (hV4) takes the form of a single, uninterrupted ventral hemifield, measured retinotopic maps of this visual area are often incomplete. Here, we test hypotheses that artefact from draining veins close to hV4 cause inverted BOLD responses that may serve to obscure a portion of the lower visual quarterfield-including the lower vertical meridian-in some hemispheres. We further test whether correcting such responses can restore the 'missing' retinotopic coverage in hV4. Subjects (N = 10) viewed bowtie, ring, drifting bar and full field flash stimuli. Functional EPIs were acquired over approximately 1.5h and analysed to reveal retinotopic maps of early visual cortex, including hV4. Normalised mean maps (which show the average EPI signal amplitude) were constructed by voxel-wise averaging of the EPI time course and used to locate venous eclipses, which can be identified by a decrease in the EPI signal caused by deoxygenated blood. Inverted responses are shown to cluster in these regions and correcting these responses improves maps of hV4 in some hemispheres, including restoring a complete hemifield map in one. A leftwards bias was found whereby 6/10 left hemisphere hV4 maps were incomplete, while this was the case in only 1/10 right hemisphere maps. Incomplete hV4 maps did not correspond with venous artefact in every instance, with incomplete maps being present in the absence of a venous eclipse and complete maps coexisting with a proximate venous eclipse. We also show that mean maps of upper surfaces (near the boundary between cortical grey matter and CSF) provide highly detailed maps of veins on the cortical surface. Results suggest that venous eclipses and inverted voxels can explain some incomplete hV4 maps, but cannot explain the remainder nor the leftwards bias in hV4 coverage reported here. },
}

@article{Spanakis2019,
  year = { 2019 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Machine-learning regression in evolutionary algorithms and image registration },
  pages = { 843--849 },
  number = { 5 },
  journal = { IET Image Processing },
  issn = { 17519659 },
  doi = { 10.1049/iet-ipr.2018.5389 },
  author = { Spanakis and Mathioudakis and Kampanis and Tsiknakis and Marias },
  abstract = { Evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non-convex with many local optima. However, their drawback is that they tend to be computationally expensive. Trying to avoid local minima can increase the computational cost. The purpose of authors' research is to minimise the duration of the image registration process. This paper presents a method to minimise the computational cost by introducing a machine learning-based variant of Harmony Search. To this end, a series of machine-learning regression methods are tested in order to find the most appropriate that minimises the cost without degrading the quality of the results. The best regression method is then incorporated in the optimisation process and is compared with two well-known ITK image registration methods. The comparison of authors' image registration method with ITK concerns both the quality of the results and the duration of the registration experiments. The comparison is done on a set of random image pairs of various sources (e.g. medical or satellite images), and the encouraging results strongly indicate that authors' method can be used in a variety of image registration applications producing quality results in significantly less time. },
}

@article{Primpke2019,
  year = { 2019 },
  volume = { 11 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automated identification and quantification of microfibres and microplastics },
  pages = { 2138--2147 },
  number = { 16 },
  journal = { Analytical Methods },
  issn = { 17599679 },
  doi = { 10.1039/c9ay00126c },
  author = { Primpke and Dias and Gerdts },
  abstract = { The ubiquitous presence of microlitter (ML), precisely microplastics (MP) and microfibres (MF) in the global environment is of growing concern for science, and society in general. Reliable methods are urgently needed for the identification and quantification of these emerging environmental pollutants. Recently a rapid Fourier transform infrared (FTIR) imaging pipeline was developed for automated identification and quantification of MP. However, although the usefulness for the quantification of MP could already be shown in several studies, microfibres could not be targeted so far by the developed analysis pipeline. In this study we present a novel approach for the simultaneous identification and quantification of MP and MF. By concentrating the sample on membrane filters and applying a BaF2 window on top of the filter, all objects-including MF-are fixed in the focal plane of the FTIR microscope. Furthermore, the analysis pipeline was augmented with algorithms which take into consideration the filamentous structure of MF. The novel analysis pipeline now allows to separate MP and MF via a preselection of fibres from the dataset by object size and shape. MP and MF are subsequently further investigated for specific polymer types and lengths/sizes. After parameter optimization the newly developed analysis approach was applied to archived samples from previous studies on treated waste water. The results were compared with respect to the original detected polymer types and numbers, but also considered MF detection. },
}

@article{Pinter2019,
  year = { 2019 },
  volume = { 171 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Polymorph segmentation representation for medical image computing },
  pages = { 19--26 },
  keywords = { 3D Slicer,DICOM,Open-source,Segmentation,Software library,Voxelization },
  journal = { Computer Methods and Programs in Biomedicine },
  issn = { 18727565 },
  doi = { 10.1016/j.cmpb.2019.02.011 },
  author = { Pinter and Lasso and Fichtinger },
  abstract = { Background and objective: Segmentation is a ubiquitous operation in medical image computing. Various data representations can describe segmentation results, such as labelmap volumes or surface models. Conversions between them are often required, which typically include complex data processing steps. We identified four challenges related to managing multiple representations: conversion method selection, data provenance, data consistency, and coherence of in-memory objects. Methods: A complex data container preserves identity and provenance of the contained representations and ensures data coherence. Conversions are executed automatically on-demand. A graph containing the implemented conversion algorithms determines each execution, ensuring consistency between various representations. The design and implementation of a software library are proposed, in order to provide a readily usable software tool to manage segmentation data in multiple data representations. A low-level core library called PolySeg implemented in the Visualization Toolkit (VTK) manages the data objects and conversions. It is used by a high-level application layer, which has been implemented in the medical image visualization and analysis platform 3D Slicer. The application layer provides advanced visualization, transformation, interoperability, and other functions. Results: The core conversion algorithms comprising the graph were validated. Several applications were implemented based on the library, demonstrating advantages in terms of usability and ease of software development in each case. The Segment Editor application provides fast, comprehensive, and easy-to-use manual and semi-automatic segmentation workflows. Clinical applications for gel dosimetry, external beam planning, and MRI-ultrasound image fusion in brachytherapy were rapidly prototyped resulting robust applications that are already in use in clinical research. The conversion algorithms were found to be accurate and reliable using these applications. Conclusions: A generic software library has been designed and developed for automatic management of multiple data formats in segmentation tasks. It enhances both user and developer experience, enabling fast and convenient manual workflows and quicker and more robust software prototyping. The software's BSD-style open-source license allows complete freedom of use of the library. },
}

@article{Muschelli2019,
  year = { 2019 },
  volume = { 20 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Neuroconductor: An R platform for medical imaging analysis },
  pages = { 218--239 },
  number = { 2 },
  keywords = { Bioinformatics,Image analysis,Statistical modelling },
  journal = { Biostatistics },
  issn = { 14684357 },
  doi = { 10.1093/biostatistics/kxx068 },
  author = { Muschelli and Gherman and Fortin and Avants and Whitcher and Clayden and Caffo and Crainiceanu },
  abstract = { Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience. },
}

@article{Mouches2019,
  year = { 2019 },
  volume = { 6 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects },
  pages = { 29 },
  number = { 1 },
  journal = { Scientific data },
  issn = { 20524463 },
  doi = { 10.1038/s41597-019-0034-5 },
  author = { Mouches and Forkert },
  abstract = { Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain. },
}

@article{Magnain2019,
  year = { 2019 },
  volume = { 224 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann's area 32 and area 21 },
  pages = { 351--362 },
  number = { 1 },
  keywords = { Human brain,Isocortex,Limbic,Neuron,Optical imaging,Tissue,Validation },
  journal = { Brain Structure and Function },
  issn = { 18632661 },
  doi = { 10.1007/s00429-018-1777-z },
  author = { Magnain and Augustinack and Tirrell and Fogarty and Frosch and Boas and Fischl and Rockland },
  abstract = { Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67{\%} for BA 32 (47{\%} for Nissl colocalization) and 60{\%} for BA 21 (52{\%} for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain. },
}

@article{Ma2019,
  year = { 2019 },
  volume = { 20 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A novel auto-positioning method in Iodine-125 seed brachytherapy driven by preoperative planning },
  pages = { 23--30 },
  number = { 6 },
  keywords = { auto-positioning,iodine-125 seed brachytherapy,preoperative planning,treatment planning system },
  journal = { Journal of Applied Clinical Medical Physics },
  issn = { 15269914 },
  doi = { 10.1002/acm2.12591 },
  author = { Ma and Yang and Jiang and Zhang and Chai },
  abstract = { Iodine-125 seed brachytherapy has great potential in the treatment of malignant tumors. However, the success of this treatment is highly dependent on the ability to accurately position the coplanar template. The aim of this study was to develop an auto-positioning system for the template with a design focus on efficiency and accuracy. In this study, an auto-positioning system was presented, which was composed of a treatment planning system (TPS) and a robot-assisted system. The TPS was developed as a control system for the robot-assisted system. Then, the robot-assisted system was driven by the output of the TPS to position the template. Contrast experiments for error validation were carried out in a computed tomography environment to compare with the traditional positioning method (TPM). Animal experiments on Sprague–Dawley rats were also carried out to evaluate the auto-positioning system. The error validation experiments and animal experiments with this auto-positioning system were successfully carried out with improved efficiency and accuracy. The error validation experiments achieved a positioning error of 1.04 ± 0.19 mm and a positioning time of 23.15 ± 2.52 min, demonstrating a great improvement compared with the TPM (2.55 ± 0.21 mm and 40.35 ± 2.99 min, respectively). The animal experiments demonstrated that the mean deviation of the seed position was 0.75 mm. The dose-volume histogram of the preoperative planning showed the same as the postoperative dosimetry validation. A novel auto-positioning system driven by preoperative planning was established, which exhibited higher efficiency and accuracy compared with the TPM. },
}

@article{Lebre2019,
  year = { 2019 },
  volume = { 110 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme },
  pages = { 42--51 },
  keywords = { CT and MRI volumes,Couinaud,Liver segmentation,Medical imaging,Vessel segmentation },
  journal = { Computers in Biology and Medicine },
  issn = { 18790534 },
  doi = { 10.1016/j.compbiomed.2019.04.014 },
  author = { Lebre and Vacavant and Grand-Brochier and Rositi and Abergel and Chabrot and Magnin },
  abstract = { Background: Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task. Methods: We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations. Results: We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. Conclusions: We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization. },
}

@article{Jung2019,
  year = { 2019 },
  volume = { 29 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Semiautomated Assessment of the Anterior Cingulate Cortex in Alzheimer's Disease },
  pages = { 376--382 },
  number = { 3 },
  keywords = { MRI,automated segmentation,brain atrophy,structural biomarker },
  journal = { Journal of Neuroimaging },
  issn = { 15526569 },
  doi = { 10.1111/jon.12598 },
  author = { Jung and Kazemifar and Bartha and Rajakumar },
  abstract = { BACKGROUND AND PURPOSE: The anterior cingulate cortex (ACC) is involved in several cognitive processes including executive function. Degenerative changes of ACC are consistently seen in Alzheimer's disease (AD). However, volumetric changes specific to the ACC in AD are not clear because of the difficulty in segmenting this region. The objectives of the current study were to develop a precise and high-throughput approach for measuring ACC volumes and to correlate the relationship between ACC volume and cognitive function in AD. METHODS: Structural T 1 -weighted magnetic resonance images of AD patients (n = 47) and age-matched controls (n = 47) at baseline and at 24 months were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database and studied using a custom-designed semiautomated segmentation protocol. RESULTS: ACC volumes obtained using the semiautomated protocol were highly correlated to values obtained from manual segmentation (r =.98) and the semiautomated protocol was considerably faster. When comparing AD and control subjects, no significant differences were observed in baseline ACC volumes or in change in ACC volumes over 24 months using the two segmentation methods. However, a change in ACC volume over 24 months did not correlate with a change in mini-mental state examination scores. CONCLUSIONS: Our results indicate that the proposed semiautomated segmentation protocol is reliable for determining ACC volume in neurodegenerative conditions including AD. },
}

@article{Hilty2019,
  year = { 2019 },
  volume = { 2 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { MicroTools enables automated quantification of capillary density and red blood cell velocity in handheld vital microscopy },
  pages = { 15 },
  number = { 1 },
  journal = { Communications Biology },
  issn = { 23993642 },
  doi = { 10.1038/s42003-019-0473-8 },
  author = { Hilty and Guerci and Ince and Toraman and Ince },
  abstract = { Direct assessment of capillary perfusion has been prioritized in hemodynamic management of critically ill patients in addition to optimizing blood flow on the global scale. Sublingual handheld vital microscopy has enabled online acquisition of moving image sequences of the microcirculation, including the flow of individual red blood cells in the capillary network. However, due to inherent content complexity, manual image sequence analysis remained gold standard, introducing inter-observer variability and precluding real-time image analysis for clinical therapy guidance. Here we introduce an advanced computer vision algorithm for instantaneous analysis and quantification of morphometric and kinetic information related to capillary blood flow in the sublingual microcirculation. We evaluated this technique in a porcine model of septic shock and resuscitation and cardiac surgery patients. This development is of high clinical relevance because it enables implementation of point-of-care goal-directed resuscitation procedures based on correction of microcirculatory perfusion in critically ill and perioperative patients. },
}

@article{Gribble2019,
  year = { 2019 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A multiscale Mueller polarimetry module for a stereo zoom microscope },
  pages = { 339--349 },
  number = { 3 },
  keywords = { Label-free imaging,Module,Mueller matrix polarimetry,Multiscale,Pathology,Stereo zoom microscope },
  journal = { Biomedical Engineering Letters },
  issn = { 2093985X },
  doi = { 10.1007/s13534-019-00116-w },
  author = { Gribble and Pinkert and Westreich and Liu and Keikhosravi and Khorasani and Nofech-Mozes and Eliceiri and Vitkin },
  abstract = { Mueller polarimetry is a quantitative polarized light imaging modality that is capable of label-free visualization of tissue pathology, does not require extensive sample preparation, and is suitable for wide-field tissue analysis. It holds promise for selected applications in biomedicine, but polarimetry systems are often constrained by limited end-user accessibility and/or long-imaging times. In order to address these needs, we designed a multiscale-polarimetry module that easily couples to a commercially available stereo zoom microscope. This paper describes the module design and provides initial polarimetry imaging results from a murine preclinical breast cancer model and human breast cancer samples. The resultant polarimetry module has variable resolution and field of view, is low-cost, and is simple to switch in or out of a commercial microscope. The module can reduce long imaging times by adopting the main imaging approach used in pathology: scanning at low resolution to identify regions of interest, then at high resolution to inspect the regions in detail. Preliminary results show how the system can aid in region of interest identification for pathology, but also highlight that more work is needed to understand how tissue structures of pathological interest appear in Mueller polarimetry images across varying spatial zoom scales. },
}

@article{Goncalves2019,
  year = { 2019 },
  volume = { 123 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Nucleation and Growth of Cavities in Hydrated Nafion Membranes under Tensile Strain: A Molecular Dynamics Study },
  pages = { 28958--28968 },
  number = { 47 },
  journal = { Journal of Physical Chemistry C },
  issn = { 19327455 },
  doi = { 10.1021/acs.jpcc.9b07101 },
  author = { Gon{\c{c}}alves and Mabuchi and Tokumasu },
  abstract = { Molecular dynamics simulations are performed to investigate the nucleation and growth of cavities in a hydrated Nafion membrane under mechanical deformation. The simulation model used in this study accurately reproduces the experimental values of the elastic modulus of the membrane as a function of water content. The results obtained from triaxial tensile tests reveal a ductile to brittle transition as the water content increases. The nucleation and growth of the cavities have been quantitatively analyzed in terms of the number and size of cavities, illustrating the ductile to brittle transition uncovered by the stress/strain curves. Further local analyses have been carried out to identify the nucleation sites. The analysis of local plasticity indicates that as the water content increases, the membrane accumulates more plastic deformation in the hydrophilic domain than in the hydrophobic domain during the rupture stage of the tensile tests. These results suggest that the water network significantly impacts the nucleation and expansion of cavities induced by mechanical deformation. Furthermore, the local mechanical properties of the Nafion membrane are evaluated. The results show that the mechanical properties are heterogeneous at the nanoscale and that the cavities nucleate in soft regions of the membrane. A statistical analysis of the local water density of nucleation sites indicates that the polymer-water interfaces are more likely to nucleate cavities. The expansion and coalescence of cavities is facilitated by the high molecular reorganization of the water network, which explains the brittle behavior of membranes with high water content. },
}

@article{Fuentes2019,
  year = { 2019 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study },
  pages = { 499--506 },
  number = { 3 },
  journal = { Journal of computer assisted tomography },
  issn = { 15323145 },
  doi = { 10.1097/RCT.0000000000000866 },
  author = { Fuentes and Ahmed and Lin and Abdel-Wahab and Kaseb and Hassan and Szklaruk and Morshid and Hazle and Qayyum and Elsayes },
  abstract = { PURPOSE: This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib. METHODS: In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods. RESULTS: When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP. CONCLUSIONS: Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations. },
}

@article{Dolly2019,
  year = { 2019 },
  volume = { 64 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Task-based image quality assessment in radiation therapy: Initial characterization and demonstration with computer-simulation study },
  pages = { 19 },
  number = { 14 },
  keywords = { geometric attribute distribution model,learning-based stochastic object models,radiation therapy,task-based image quality assessment,therapeutic operating characteristic curve },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/ab2dc5 },
  author = { Dolly and Lou and Anastasio and Li },
  abstract = { In the majority of current radiation therapy (RT) applications, image quality is still assessed subjectively or by utilizing physical measures. A novel theory that applies objective task-based image quality assessment in radiation therapy (IQA-in-RT) was recently proposed, in which the area under the therapeutic operating characteristic curve (AUTOC) was employed as the figure-of-merit (FOM) for evaluating RT effectiveness. Although theoretically more appealing than conventional subjective or physical measures, a comprehensive implementation and evaluation of this novel task-based IQA-in-RT theory is required for its further application in improving clinical RT. In this work, a practical and modular IQA-in-RT framework is presented for implementing this theory for the assessment of imaging components on the basis of RT treatment outcomes. Computer-simulation studies are conducted to demonstrate the feasibility and utility of the proposed IQA-in-RT framework in optimizing x-ray computed tomography (CT) pre-treatment imaging, including the optimization of CT imaging dose and image reconstruction parameters. The potential advantages of optimizing imaging components in the RT workflow by use of the AUTOC as the FOM are also compared against those of other physical measures. The results demonstrate that optimization using the AUTOC leads to selecting different parameters from those indicated by physical measures, potentially improving RT performance. The sources of systemic randomness and bias that affect the determination of the AUTOC are also analyzed. The presented work provides a practical solution for the further investigation and analysis of the task-based IQA-in-RT theory and advances its applications in improving RT clinical practice and cancer patient care. },
}

@article{Choi2019,
  year = { 2019 },
  volume = { 20 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Relationship between abnormal hyperintensity on T2-weighted images around developmental venous anomalies and magnetic susceptibility of their collecting veins: In-vivo quantitative susceptibility mapping study },
  pages = { 662--670 },
  number = { 4 },
  keywords = { Developmental venous anomaly,Magnetic resonance imaging,Quantitative susceptibility mapping,Vascular malformation },
  journal = { Korean Journal of Radiology },
  issn = { 12296929 },
  doi = { 10.3348/kjr.2018.0685 },
  author = { Choi and Jang and Nam and Shin and Choi and Jung and Ahn and Kim },
  abstract = { Objective: A developmental venous anomaly (DVA) is a vascular malformation of ambiguous clinical significance. We aimed to quantify the susceptibility of draining veins ($\chi$vein) in DVA and determine its significance with respect to oxygen metabolism using quantitative susceptibility mapping (QSM). Materials and Methods: Brain magnetic resonance imaging of 27 consecutive patients with incidentally detected DVAs were retrospectively reviewed. Based on the presence of abnormal hyperintensity on T2-weighted images (T2WI) in the brain parenchyma adjacent to DVA, the patients were grouped into edema (E+, n = 9) and non-edema (E-, n = 18) groups. A 3T MR scanner was used to obtain fully flow-compensated gradient echo images for susceptibility-weighted imaging with source images used for QSM processing. The $\chi$vein was measured semi-automatically using QSM. The normalized $\chi$vein was also estimated. Clinical and MR measurements were compared between the E+ and E- groups using Student's t-test or Mann-Whitney U test. Correlations between the $\chi$vein and area of hyperintensity on T2WI and between $\chi$vein and diameter of the collecting veins were assessed. The correlation coefficient was also calculated using normalized veins. Results: The DVAs of the E+ group had significantly higher $\chi$vein (196.5 ± 27.9 vs. 167.7 ± 33.6, p = 0.036) and larger diameter of the draining veins (p = 0.006), and patients were older (p = 0.006) than those in the E- group. The $\chi$vein was also linearly correlated with the hyperintense area on T2WI (r = 0.633, 95{\%} confidence interval 0.333-0.817, p {\textless} 0.001). Conclusion: DVAs with abnormal hyperintensity on T2WI have higher susceptibility values for draining veins, indicating an increased oxygen extraction fraction that might be associated with venous congestion. },
}

@article{Buzzatti2019,
  year = { 2019 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Four-dimensional CT as a valid approach to detect and quantify kinematic changes after selective ankle ligament sectioning },
  pages = { 9 },
  number = { 1 },
  journal = { Scientific Reports },
  issn = { 20452322 },
  doi = { 10.1038/s41598-018-38101-5 },
  author = { Buzzatti and Keelson and Apperloo and Scheerlinck and Baeyens and {Van Gompel} and Vandemeulebroucke and Maeseneer and Mey and Buls and Cattrysse },
  abstract = { The objective of the current study was to explore the potential of dynamic computed tomography to detect kinematic changes, induced by sequential sectioning of the lateral collateral ligaments of the ankle, during full motion sequence of the talocrural joint. A custom-made device was used to induce cyclic controlled ankle inversion movement in one fresh frozen cadaver leg. A 256-slice CT scanner was used to investigate four different scenarios. Scenario 1 with all ligaments intact was first investigated followed by sequential section of the anterior talo-fibular ligament (Scenario 2), the calcaneo-fibular ligament (Scenario 3) and posterior talo-fibular ligament (Scenario 4). Off-line image processing based on semi-automatic segmentation and bone rigid registration was performed. Motion parameters such as translation, rotational angles and orientation and position of the axis of rotation were calculated. Differences between scenarios were calculated. Progressive increase of cranio-caudal displacement up to 3.9 mm and flexion up to 10° compared to Scenario 1 were reported. Progressive changes in orientation (up to 20.6°) and position (up to 4.1 mm) of the axis of rotation were also shown. Estimated effective dose of 0.005 mSv (1.9 mGy CTDIvol) was reported. This study demonstrated that kinematic changes due to the absence of ligament integrity can be detected with 4DCT with minimal radiation exposure. Identifying abnormal kinematic patterns could have future application in helping clinicians to choose patients' optimal treatment. Therefore, further studies with bigger in vitro sample sizes and consequent investigations in vivo are recommended to confirm the current findings. },
}

@article{Balasubramanian2019,
  year = { 2019 },
  volume = { 233 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers },
  pages = { 506--514 },
  number = { 5 },
  keywords = { Glaucoma,blood vessel,classifier,clustering,feature selection,fundus image,thresholding },
  journal = { Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine },
  issn = { 20413033 },
  doi = { 10.1177/0954411919835856 },
  author = { Balasubramanian and Ananthamoorthy },
  abstract = { Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus images are initially preprocessed for any noise reduction, image enhancement and contrast adjustment. The two-dimensional Gabor Wavelets and Partition Clustering is employed on the preprocessed image to extract the blood vessels. Finally, the combined hybrid features comprising statistical textural, intensity and vessel morphological features, extracted from the image, are used to detect glaucomatous abnormality through the classifiers. A crisp decision can be taken depending on the classifying rates of the classifiers. Public databases RIM-ONE and high-resolution fundus and local datasets are used for evaluation with threefold cross validation. The evaluation is based on performance metrics through accuracy, sensitivity and specificity. The evaluation of hybrid features obtained an overall accuracy of 97{\%} when tested using classifiers. The support vector machine classifier is able to achieve an accuracy of 93.33{\%} on high-resolution fundus, 93.8{\%} on RIM-ONE dataset and 95.3{\%} on local dataset. For extreme learning machine classifier, the accuracy is 95.1{\%} on high-resolution fundus, 97.8{\%} on RIM-ONE and 96.8{\%} on local dataset. An accuracy of 94.5{\%} on high-resolution fundus 92.5{\%} on RIM-ONE and 94.2{\%} on local dataset is obtained for the random forest classifier. Validation of the experiment results indicate that the hybrid features can be deployed in supervised classifiers to discriminate retinal abnormalities effectively. },
}

@article{Belmonte2018,
  year = { 2019 },
  volume = { 11427 LNCS },
  url = { http://arxiv.org/abs/1811.05677 },
  title = { Voxlogica: A spatial model checker for declarative image analysis },
  pages = { 281--298 },
  month = { nov },
  keywords = { Closure spaces,Medical Imaging,Model checking,Spatial logics },
  journal = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  issn = { 16113349 },
  isbn = { 9783030174613 },
  eprint = { 1811.05677 },
  doi = { 10.1007/978-3-030-17462-0_16 },
  author = { Belmonte and Ciancia and Latella and Massink },
  arxivid = { 1811.05677 },
  archiveprefix = { arXiv },
  abstract = { Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis. We explore a new domain, namely (semi-)automatic contouring in Medical Imaging, introducing the tool VoxLogicA which merges the state-of-the-art library of computational imaging algorithms ITK with the unique combination of declarative specification and optimised execution provided by spatial logic model checking. The result is a rapid, logic based analysis development methodology. The analysis of an existing benchmark of medical images for segmentation of brain tumours shows that simple VoxLogicA analysis can reach state-of-the-art accuracy, competing with best-in-class algorithms, with the advantage of explainability and easy replicability. Furthermore, due to a two-orders-of-magnitude speedup compared to the existing general-purpose spatio-temporal model checker topochecker, VoxLogicA enables interactive development of analysis of 3D medical images, which can greatly facilitate the work of professionals in this domain. },
}

@article{RN842,
  year = { 2019 },
  volume = { 22 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044478175{\&}doi=10.1007{\%}2Fs10586-018-2567-3{\&}partnerID=40{\&}md5=43af833a57941962e238b16f43fb845f },
  type = { Journal Article },
  title = { Non-rigid registration of multi-phase liver CT data using fully automated landmark detection and TPS deformation },
  pages = { 15305--15319 },
  keywords = { Edge textures,Landmark detection,Liver registration,TPS },
  journal = { Cluster Computing },
  issn = { 15737543 },
  doi = { 10.1007/s10586-018-2567-3 },
  author = { Zhang and Tan and Gao and Wu and Zhou and Fujita },
  abstract = { In case of the complicated anatomical structure of the liver, landmark points on a three dimensional (3D) liver surface is hardly distinguished as corresponding pairs visually and automated landmark placing will be extremely time saving for liver registration. This paper presents a fully automated landmark detection method to register livers on multi-phase computed tomography (CT) images. Edge texture features and Support Vector Machine (SVM) are applied to detect the discriminated landmarks of the liver, including both surface and internal points. Using the information of liver shape, 3D gray level co-occurrence matrix is calculated into texture features, from which the most informatics there features are selected by our optimization algorithm for choosing a sub-set of features from a high dimensional feature set. Then automated landmarks detection begins at scanning surface points on the pre-contrast and portal venous phase images, where positive outputs of the SVM classifier are regarded as initial candidates and final candidates are obtained by eliminating false positives (FPs). Finally, relied on the detected landmarks, thin plate splines (TPS) algorithm is used to register livers. Five surface landmarks, together with internal landmarks of the liver center from every 25 mm slice interval, can be detected automatically with sensitivity of 88.33{\%} and accuracy of 98.5{\%}. Surface-based mean error (SME) is decreased from 3.80 to 2.87 mm on average, while SME value has increased 32.4 and 8.0{\%} on average respectively when comparing with the rigid and B-spline methods. The results demonstrate that edge textures and SVM classifier are effective in the automated landmark detection. Together with TPS algorithm, fully automated liver registration is able to be achieved on multi-phase CT images. },
}

@article{RN808,
  year = { 2019 },
  volume = { 121 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions },
  pages = { 9 },
  keywords = { CT-radiomics features,Lung lesion,PET metabolic parameters,Potential feature },
  journal = { European Journal of Radiology },
  issn = { 18727727 },
  doi = { 10.1016/j.ejrad.2019.108735 },
  author = { Zhang and Zhu and Cai and Jiang and Li and Yang and Yu and Jiang and Wang and Xu and Chai and Zhang and Tang },
  abstract = { Purpose: The study is to explore potential features and develop classification models for distinguishing benign and malignant lung lesions based on CT-radiomics features and PET metabolic parameters extracted from PET/CT images. Materials and methods: A retrospective study was conducted in baseline 18 F-flurodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 135 patients. The dataset was utilized for feature extraction of CT-radiomics features and PET metabolic parameters based on volume of interest, then went through feature selection and model development with strategy of five-fold cross-validation. Specifically, model development used support vector machine, PET metabolic parameters selection used Akaike's information criterion, and CT-radiomics were reduced by the least absolute shrinkage and selection operator method then forward selection approach. The diagnostic performances of CT-radiomics, PET metabolic parameters and combination of both were illustrated by receiver operating characteristic (ROC) curves, and compared by Delong test. Five groups of selected PET metabolic parameters and CT-radiomics were counted, and potential features were found and analyzed with Mann-Whitney U test. Results: The CT-radiomics, PET metabolic parameters, and combination of both among five subsets showed mean area under the curve (AUC) of 0.820 ± 0.053, 0.874 ± 0.081, and 0.887 ± 0.046, respectively. No significant differences in ROC among models were observed through pairwise comparison in each fold (P-value from 0.09 to 0.81, Delong test). The potential features were found to be SurfaceVolumeRatio and SUVpeak (P {\textless} 0.001 of both, U test). Conclusion: The classification models developed by CT-radiomics features and PET metabolic parameters based on PET/CT images have substantial diagnostic capacity on lung lesions. },
}

@article{RN810,
  year = { 2019 },
  volume = { 32 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research (Journal of Digital Imaging, (2018), 31, 3, (290-303), 10.1007/s10278-017-0037-8) },
  pages = { 1118 },
  number = { 6 },
  journal = { Journal of Digital Imaging },
  issn = { 1618727X },
  doi = { 10.1007/s10278-018-0165-9 },
  author = { Yaniv and Lowekamp and Johnson and Beare },
  abstract = { This paper had published originally without open access, but has since been republished with open access. },
}

@article{RN849,
  year = { 2019 },
  volume = { 46 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063261918{\&}doi=10.1002{\%}2Fmp.13471{\&}partnerID=40{\&}md5=7e6f21940c6d8f7bbd4b733b927a0667 },
  type = { Journal Article },
  title = { Construction of patient-specific computational models for organ dose estimation in radiological imaging },
  pages = { 2403--2411 },
  number = { 5 },
  keywords = { Monte Carlo simulations,computational models,radiation dose,radiological imaging },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.13471 },
  author = { Xie and Akhavanallaf and Zaidi },
  abstract = { Purpose: Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ-level dose assessment in x-ray computed tomography (CT) imaging. Methods: Regional voxel models representing reference patient-specific computational phantoms were generated through image segmentation of CT images for four patients. The best-fitting anthropomorphic phantoms were selected from a previously developed comprehensive phantom library according to patient's anthropometric parameters, then registered to the anatomical masks (skeleton, lung, and body contour) of patients to produce a patient-specific whole-body phantom. Well-established image registration metrics including Jaccard's coefficients for each organ, organ mass, body perimeter, organ-surface distance, and effective diameter are compared between the reference patient model, registered model, and anchor phantoms. A previously validated Monte Carlo code is utilized to calculate the absorbed dose in target organs along with the effective dose delivered to patients. The calculated absorbed doses from the reference patient models are then compared with the produced personalized model, anchor phantom, and those reported by commercial dose monitoring systems. Results: The evaluated organ-surface distance and body effective diameter metrics show a mean absolute difference between patient regional voxel models, serving as reference, and patient-specific models around 4.4{\%} and 4.5{\%}, respectively. Organ-level radiation doses of patient-specific models are in good agreement with those of the corresponding patient regional voxel models with a mean absolute difference of 9.1{\%}. The mean absolute difference of organ doses for the best-fitting model extracted from the phantom library and Radimetrics™ commercial dose tracking software are 15.5{\%} and 41.1{\%}, respectively. Conclusion: The results suggest that the proposed methodology improves the accuracy of organ-level dose estimation in CT, especially for extreme cases [high body mass index (BMI) and large skeleton]. Patient-specific radiation dose calculation and risk assessment can be performed using the proposed methodology for both monitoring of cumulative radiation exposure of patients and epidemiological studies. Further validation using a larger database is warranted. },
}

@article{RN854,
  year = { 2019 },
  volume = { 14 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064242516{\&}doi=10.1371{\%}2Fjournal.pone.0215137{\&}partnerID=40{\&}md5=4418e4939d68c3c8368a16476e37b7fb },
  type = { Journal Article },
  title = { Quanfima: An open source Python package for automated fiber analysis of biomaterials },
  number = { 4 },
  journal = { PLoS ONE },
  issn = { 19326203 },
  doi = { 10.1371/journal.pone.0215137 },
  author = { Shkarin and Shkarin and Shkarina and Cecilia and Surmenev and Surmeneva and Weinhardt and Baumbach and Mikut },
  abstract = { Hybrid 3D scaffolds composed of different biomaterials with fibrous structure or enriched with different inclusions (i.e., nano- and microparticles) have already demonstrated their positive effect on cell integration and regeneration. The analysis of fibers in hybrid biomaterials, especially in a 3D space is often difficult due to their various diameters (from micro to nanoscale) and compositions. Though biomaterials processing workflows are implemented, there are no software tools for fiber analysis that can be easily integrated into such workflows. Due to the demand for reproducible science with Jupyter notebooks and the broad use of the Python programming language, we have developed the new Python package quanfima offering a complete analysis of hybrid biomaterials, that include the determination of fiber orientation, fiber and/or particle diameter and porosity. Here, we evaluate the provided tensor-based approach on a range of generated datasets under various noise conditions. Also, we show its application to the X-ray tomography datasets of polycaprolactone fibrous scaffolds pure and containing silicate-substituted hydroxyapatite microparticles, hydrogels enriched with bioglass contained strontium and alpha-tricalcium phosphate microparticles for bone tissue engineering and porous cryogel 3D scaffold for pancreatic cell culturing. The results obtained with the help of the developed package demonstrated high accuracy and performance of orientation, fibers and microparticles diameter and porosity analysis. },
}

@article{RN846,
  year = { 2019 },
  volume = { 43 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068766678{\&}doi=10.1007{\%}2Fs10916-019-1401-7{\&}partnerID=40{\&}md5=5d6cacfa42ec287f82160fe89c70fe3a },
  type = { Journal Article },
  title = { Age Prediction Based on Brain MRI Image: A Survey },
  pmid = { 31297614 },
  number = { 8 },
  keywords = { Age prediction,BAE,Brain MRI,Brain age,Chronological age,Deep Learning,Image processing,Machine Learning },
  journal = { Journal of Medical Systems },
  issn = { 1573689X },
  doi = { 10.1007/s10916-019-1401-7 },
  author = { Sajedi and Pardakhti },
  abstract = { Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs, knee radiographs, dental images and etc. Most of the age prediction researches have mainly been based on images. Since the image processing and Machine Learning (ML) techniques have grown up, the investigations were led to use them in age prediction problem. The implementations would be used in different fields, especially in medical applications. Brain Age Estimation (BAE) has attracted more attention in recent years and it would be so helpful in early diagnosis of some neurodegenerative diseases such as Alzheimer, Parkinson, Huntington, etc. BAE is performed on Magnetic Resonance Imaging (MRI) images to compute the brain ages. Studies based on brain MRI shows that there is a relation between accelerated aging and accelerated brain atrophy. This refers to the effects of neurodegenerative diseases on brain structure while making the whole of it older. This paper reviews and summarizes the main approaches for age prediction based on brain MRI images including preprocessing methods, useful tools used in different research works and the estimation algorithms. We categorize the BAE methods based on two factors, first the way of processing MRI images, which includes pixel-based, surface-based, or voxel-based methods and second, the generation of ML algorithms that includes traditional or Deep Learning (DL) methods. The modern techniques as DL methods help MRI based age prediction to get results that are more accurate. In recent years, more precise and statistical ML approaches have been utilized with the help of related tools for simplifying computations and getting accurate results. Pros and cons of each research and the challenges in each work are expressed and some guidelines and deliberations for future research are suggested. },
}

@article{RN807,
  year = { 2019 },
  volume = { 6 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Towards standardization of absolute SPECT/CT quantification: a multi-center and multi-vendor phantom study },
  pages = { 14 },
  number = { 1 },
  keywords = { SPECT/CT,absolute quantification,performance evaluation,recovery coefficient },
  journal = { EJNMMI Physics },
  issn = { 21977364 },
  doi = { 10.1186/s40658-019-0268-5 },
  author = { Peters and Werf and Segbers and Velden and Wierts and Blokland and Konijnenberg and Lazarenko and Visser and Gotthardt },
  abstract = { Abstract: Absolute quantification of radiotracer distribution using SPECT/CT imaging is of great importance for dosimetry aimed at personalized radionuclide precision treatment. However, its accuracy depends on many factors. Using phantom measurements, this multi-vendor and multi-center study evaluates the quantitative accuracy and inter-system variability of various SPECT/CT systems as well as the effect of patient size, processing software and reconstruction algorithms on recovery coefficients (RC). Methods: Five SPECT/CT systems were included: Discovery™ NM/CT 670 Pro (GE Healthcare), Precedence™ 6 (Philips Healthcare), Symbia Intevo™, and Symbia™ T16 (twice) (Siemens Healthineers). Three phantoms were used based on the NEMA IEC body phantom without lung insert simulating body mass indexes (BMI) of 25, 28, and 47 kg/m2. Six spheres (0.5–26.5 mL) and background were filled with 0.1 and 0.01 MBq/mL 99mTc-pertechnetate, respectively. Volumes of interest (VOI) of spheres were obtained by a region growing technique using a 50{\%} threshold of the maximum voxel value corrected for background activity. RC, defined as imaged activity concentration divided by actual activity concentration, were determined for maximum (RCmax) and mean voxel value (RCmean) in the VOI for each sphere diameter. Inter-system variability was expressed as median absolute deviation (MAD) of RC. Acquisition settings were standardized. Images were reconstructed using vendor-specific 3D iterative reconstruction algorithms with institute-specific settings used in clinical practice and processed using a standardized, in-house developed processing tool based on the SimpleITK framework. Additionally, all data were reconstructed with a vendor-neutral reconstruction algorithm (Hybrid Recon™; Hermes Medical Solutions). Results: RC decreased with decreasing sphere diameter for each system. Inter-system variability (MAD) was 16 and 17{\%} for RCmean and RCmax, respectively. Standardized reconstruction decreased this variability to 4 and 5{\%}. High BMI hampers quantification of small lesions ({\textless} 10 ml). Conclusion: Absolute SPECT quantification in a multi-center and multi-vendor setting is feasible, especially when reconstruction protocols are standardized, paving the way for a standard for absolute quantitative SPECT. },
}

@article{RN794,
  year = { 2019 },
  volume = { 182 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning },
  pages = { 8 },
  keywords = { Decision support,Stereotaxy,Vessel segmentation },
  journal = { Computer Methods and Programs in Biomedicine },
  issn = { 18727565 },
  doi = { 10.1016/j.cmpb.2019.105037 },
  author = { Neumann and Campos and Younes and Jakobs and Unterberg and Kiening and Hubert },
  abstract = { Background and objective: Stereotactical procedures require exact trajectory planning to avoid blood vessels in the trajectory path. Innovation in imaging and image recognition techniques have facilitated the automatic detection of blood vessels during the planning process and may improve patient safety in the future. To assess the feasibility of a vessel detection and warning system using currently available imaging and vessel segmentation techniques. Methods: Image data were acquired from post-contrast, isovolumetric T1-weighted sequences (T1CE) and time.-of-flight MR angiography at 3T or 7T from a total of nine subjects. Vessel segmentation by a combination of a vessel-enhancement filter with subsequent level-set segmentation was evaluated using three different methods (Vesselness, FastMarching and LevelSet) in 45 stereotactic trajectories. Segmentation results were compared to a gold-standard of manual segmentation performed jointly by two human experts. Results: The LevelSet method performed best with a mean interclass correlation coefficient (ICC) of 0.76 [0.73, 0.81] compared to the FastMarching method with ICC 0.70 [0.67, 0.73] respectively. The Vesselness algorithm achieved clearly inferior overall performance with a mean ICC of 0.56 [0.53, 0.59]. The differences in mean ICC between all segmentation methods were statistically significant (p {\textless} 0.001 with post-hoc p {\textless} 0.026). The LevelSet method performed likewise good in MPRAGE and 3T-TOF images and excellent in 7T-TOF image data. The negative predictive value (NPV) was very high ({\textgreater}97{\%}) for all methods and modalities. Positive predictive values (PPV) were found in the overall range of 65–90{\%} likewise depending on algorithm and modality. This pattern reflects the disposition of all segmentation methods – in case of misclassification - to produce preferentially false-positive than false-negative results. In a clinical setting, two to three potential collision warnings would be given per trajectory on average with a PPV of around 50{\%}. Conclusions: It is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Both, T1CE and MRA sequences are suitable as image data for such an application. },
}

@article{RN862,
  year = { 2019 },
  volume = { 41 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074644073{\&}doi=10.1137{\%}2F18M1207818{\&}partnerID=40{\&}md5=e3c350f33ddb25e3d2f751778e14fc3c },
  type = { Journal Article },
  title = { Claire: A distributed-memory solver for constrained large deformation diffeomorphic image registration },
  pages = { C548--C584 },
  number = { 5 },
  keywords = { Diffeomorphic image registration,Distributed-memory algorithm,KKT preconditioner,LDDMM,Newton-Krylov method,Optimal control,PDE-constrained optimization },
  journal = { SIAM Journal on Scientific Computing },
  issn = { 10957197 },
  eprint = { 1808.04487 },
  doi = { 10.1137/18M1207818 },
  author = { Mang and Gholami and Davatzikos and Biros },
  arxivid = { 1808.04487 },
  archiveprefix = { arXiv },
  abstract = { With this work we release $\backslash$ttC $\backslash$ttL $\backslash$ttA $\backslash$ttI $\backslash$ttR $\backslash$ttE, a distributed-memory implementation of an effective solver for constrained large deformation diffeomorphic image registration problems in three dimensions. We consider an optimal control formulation. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousands of distributed memory nodes on high-end clusters. We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver. We test registration performance on synthetic and real data. We demonstrate registration accuracy on several neuroimaging datasets. We compare the performance of our scheme against different flavors of the $\backslash$ttD $\backslash$tte $\backslash$ttm $\backslash$tto $\backslash$ttn $\backslash$tts algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5$\backslash$times with a peak performance of up to 17$\backslash$times compared to our former work. },
}

@article{RN860,
  year = { 2019 },
  volume = { 7 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077958932{\&}doi=10.1109{\%}2FACCESS.2019.2943485{\&}partnerID=40{\&}md5=daba943919bc91c3fa945bebda516afa },
  type = { Journal Article },
  title = { Feature-Based Deformable Registration Using Minimal Spanning Tree for Prostate MR Segmentation },
  pages = { 138645--138656 },
  keywords = { Deformable registration,local self-similarity,minimal spanning tree,patch-based label fusion,prostate segmentation,$\alpha$-mutual information },
  journal = { IEEE Access },
  issn = { 21693536 },
  doi = { 10.1109/ACCESS.2019.2943485 },
  author = { Lu and Zha and Qiao and Wang },
  abstract = { Automatic and accurate segmentation of the prostate is still a challenging task due to intensity inhomogeneity and complicated deformation of MR images. To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. First, to reduce the amount of edges in entropic graph, a modified {\$}\backslashalpha {\$} -mutual information ( {\$}\backslashalpha {\$} -MI) based on fast minimal spanning tree (MST) is implemented for deformable registration. Second, localized {\$}\backslashalpha {\$} -MI allowing for the spatial information is proposed with the stochastic gradient optimization, and the feature space is encoded by a sparse auto-encoder. Finally, a multi-scale descriptor utilizing local self-similarity is integrated into the patch-based label fusion to obtain final segmentation. Experiments were performed on two subsets of totally 46 T2-weighted prostate MR images from 46 patients. Compared to {\$}\backslashalpha {\$} -MI based on {\$}{\{}k{\}}{\$} -nearest neighbor graph, the registration time of {\$}\backslashalpha {\$} -MI based on fast MST can be reduced by almost half. The median Dice overlap of registration using localized {\$}\backslashalpha {\$} -MI on one subset is shown to improve significantly from 0.725 to 0.764 ( {\$}p=1.14\backslashtimes 10{\^{}}{\{}-5{\}}{\$} ), compared to using {\$}\backslashalpha {\$} -MI without the spatial information. The median Dice overlap of prostate segmentation using the proposed method on 20 testing images of another subset is 0.871, and the median Hausdorff distance is 8.013 mm, which demonstrate a comparable accuracy to state-of-the-art methods. },
}

@article{RN818,
  year = { 2019 },
  volume = { 6 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Development and evaluation of a method for segmentation of cardiac, subcutaneous, and visceral adipose tissue from Dixon magnetic resonance images },
  pages = { 1 },
  number = { 01 },
  journal = { Journal of Medical Imaging },
  issn = { 2329-4310 },
  doi = { 10.1117/1.jmi.6.1.014004 },
  author = { Klingensmith and Elliott and Givan and Faszold and Mahan and Doedtman },
  abstract = { Magnetic resonance imaging (MRI) has evolved into the gold standard for quantifying excess adiposity, but reliable, efficient use in longitudinal studies requires analysis of large numbers of images. The objective of this study is to develop and evaluate a segmentation method designed to identify cardiac, subcutaneous, and visceral adipose tissue (VAT) in Dixon MRI scans. The proposed method is evaluated using 10 scans from volunteer females 18- to 35-years old, with body mass indexes between 30 and 39.99  kg  /  m2. Cross-sectional area (CSA) for cardiac adipose tissue (CAT), subcutaneous adipose tissue (SAT), and VAT, is compared to manually-traced results from three observers. Comparisons of CSA are made in 191 images for CAT, 394 images for SAT, and 50 images for VAT. The segmentation correlated well with respect to average observer CSA with Pearson correlation coefficient (R2) values of 0.80 for CAT, 0.99 for SAT, and 0.99 for VAT. The proposed method provides accurate segmentation of CAT, SAT, and VAT and provides an option to support longitudinal studies of obesity intervention. },
}

@article{RN812,
  year = { 2019 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Motion of dust particles in dry snow under temperature gradient metamorphism },
  pages = { 2345--2359 },
  number = { 9 },
  journal = { Cryosphere },
  issn = { 19940424 },
  doi = { 10.5194/tc-13-2345-2019 },
  author = { Hagenmuller and Flin and Dumont and Tuzet and Peinke and Lapalus and Dufour and Roulle and P{\'{e}}zard and Voisin and Ando and {Rolland Du Roscoat} and Charrier },
  abstract = { The deposition of light-absorbing particles (LAPs) such as mineral dust and black carbon on snow is responsible for a highly effective climate forcing, through darkening of the snow surface and associated feedbacks. The interplay between post-depositional snow transformation (metamorphism) and the dynamics of LAPs in snow remains largely unknown. We obtained time series of X-ray tomography images of dust-contaminated samples undergoing dry snow metamorphism at around-2C. They provide the first observational evidence that temperature gradient metamorphism induces dust particle motion in snow, while no movement is observed under isothermal conditions. Under temperature gradient metamorphism, dust particles can enter the ice matrix due to sublimation-condensation processes and spread down mainly by falling into the pore space. Overall, such motions might reduce the radiative impact of dust in snow, in particular in arctic regions where temperature gradient metamorphism prevails. },
}

@article{RN786,
  year = { 2019 },
  volume = { 60 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { RIDOS: A new system for online computation of the delivered dose distributions in scanning ion beam therapy },
  pages = { 139--149 },
  keywords = { Dose delivery,GPU,Online dose computation,Pencil beam scanning },
  journal = { Physica Medica },
  issn = { 1724191X },
  doi = { 10.1016/j.ejmp.2019.03.029 },
  author = { Giordanengo and Vignati and Attili and Ciocca and Donetti and Fausti and Manganaro and Milian and Molinelli and Monaco and Russo and Sacchi and {Varasteh Anvar} and Cirio },
  abstract = { Purpose: To describe a new system for scanned ion beam therapy, named RIDOS (Real-time Ion DOse planning and delivery System), which performs real time delivered dose verification integrating the information from a clinical beam monitoring system with a Graphic Processing Unit (GPU) based dose calculation in patient Computed Tomography. Methods: A benchmarked dose computation algorithm for scanned ion beams has been parallelized and adapted to run on a GPU architecture. A workstation equipped with a NVIDIA GPU has been interfaced through a National Instruments PXI-crate with the dose delivery system of the Italian National Center of Oncological Hadrontherapy (CNAO) to receive in real-time the measured beam parameters. Data from a patient monitoring system are also collected to associate the respiratory phases with each spot during the delivery of the dose. Using both measured and planned spot properties, RIDOS evaluates during the few seconds of inter-spill time the cumulative delivered and prescribed dose distributions and compares them through a fast $\gamma$-index algorithm. Results: The accuracy of the GPU-based algorithms was assessed against the CPU-based ones and the differences were found below 1‰. The cumulative planned and delivered doses are computed at the end of each spill in about 300 ms, while the dose comparison takes approximatively 400 ms. The whole operation provides the results before the next spill starts. Conclusions: RIDOS system is able to provide a fast computation of the delivered dose in the inter-spill time of the CNAO facility and allows to monitor online the dose deposition accuracy all along the treatment. },
}

@article{RN814,
  year = { 2019 },
  volume = { 81 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density },
  pages = { 1739--1754 },
  number = { 3 },
  keywords = { susceptibility mapping,trabecular bone density },
  journal = { Magnetic Resonance in Medicine },
  issn = { 15222594 },
  doi = { 10.1002/mrm.27531 },
  author = { Diefenbach and Meineke and Ruschke and Baum and Gersing and Karampinos },
  abstract = { Purpose: To develop a methodological framework to simultaneously measure R2* and magnetic susceptibility in trabecularized yellow bone marrow and to investigate the sensitivity of Quantitative Susceptibility Mapping (QSM) for measuring trabecular bone density using a non-UTE multi-gradient echo sequence. Methods: The ankle of 16 healthy volunteers and two patients was scanned using a time-interleaved multi-gradient-echo (TIMGRE) sequence. After field mapping based on water–fat separation methods and background field removal based on the Laplacian boundary value method, three different QSM dipole inversion schemes were implemented. Mean susceptibility values in regions of different trabecular bone density in the calcaneus were compared to the corresponding values in the R2* maps, bone volume to total volume ratios (BV/TV) estimated from high resolution imaging (in 14 subjects), and CT attenuation (in two subjects). In addition, numerical simulations were performed in a simplified trabecular bone model of randomly positioned spherical bone inclusions to verify and compare the scaling of R2* and susceptibility with BV/TV. Results: Differences in calcaneus trabecularization were well depicted in susceptibility maps, in good agreement with high-resolution MR and CT images. Simulations and in vivo scans showed a linear relationship of measured susceptibility with BV/TV and R2*. The ankle in vivo results showed a strong linear correlation between susceptibility and R2* (R2 = 0.88, p {\textless} 0.001) with a slope and intercept of −0.004 and 0.2 ppm, respectively. Conclusions: A method for multi-paramteric mapping, including R2* -mapping and QSM was developed for measuring trabecularized yellow bone marrow, showing good sensitivity of QSM for measuring trabecular bone density. },
}

@article{RN843,
  year = { 2019 },
  volume = { 35 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045951942{\&}doi=10.1007{\%}2Fs00371-018-1511-0{\&}partnerID=40{\&}md5=a294525f068f2ad564fa94a0fcd267bf },
  type = { Journal Article },
  title = { Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering },
  pages = { 1461--1474 },
  number = { 10 },
  keywords = { Bone segmentation,Cone beam computed tomography,Graph clustering,Mandible,Skull,Super-voxels },
  journal = { Visual Computer },
  issn = { 01782789 },
  doi = { 10.1007/s00371-018-1511-0 },
  author = { {Cuadros Linares} and Bianchi and Raveli and {Batista Neto} and Hamann },
  abstract = { Cone beam computed tomography (CBCT) is a medical imaging technique employed for diagnosis and treatment of patients with cranio-maxillofacial deformities. CBCT 3D reconstruction and segmentation of bones such as mandible or maxilla are essential procedures in surgical and orthodontic treatments. However, CBCT image processing may be impaired by features such as low contrast, inhomogeneity, noise and artifacts. Besides, values assigned to voxels are relative Hounsfield units unlike traditional computed tomography (CT). Such drawbacks render CBCT segmentation a difficult and time-consuming task, usually performed manually with tools designed for medical image processing. We present an interactive two-stage method for the segmentation of CBCT: (i) we first perform an automatic segmentation of bone structures with super-voxels, allowing a compact graph representation of the 3D data; (ii) next, a user-placed seed process guides a graph partitioning algorithm, splitting the extracted bones into mandible and skull. We have evaluated our segmentation method in three different scenarios and compared the results with ground truth data of the mandible and the skull. Results show that our method produces accurate segmentation and is robust to changes in parameters. We also compared our method with two similar segmentation strategy and showed that it produces more accurate segmentation. Finally, we evaluated our method for CT data of patients with deformed or missing bones and the segmentation was accurate for all data. The segmentation of a typical CBCT takes in average 5 min, which is faster than most techniques currently available. },
}

@article{RN840,
  year = { 2019 },
  volume = { 10 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076709753{\&}doi=10.3389{\%}2Ffphys.2019.01357{\&}partnerID=40{\&}md5=06fa9637c496f5d61d2f175943a787c0 },
  type = { Journal Article },
  title = { Assessment of the Alveolar Capillary Network in the Postnatal Mouse Lung in 3D Using Serial Block-Face Scanning Electron Microscopy },
  keywords = { 3D reconstruction,capillary network,lung,segmentation,serial block-face scanning electron microscopy },
  journal = { Frontiers in Physiology },
  issn = { 1664042X },
  doi = { 10.3389/fphys.2019.01357 },
  author = { Buchacker and M{\"{u}}hlfeld and Wrede and Wagner and Beare and McCormick and Grothausmann },
  abstract = { The alveolar capillary network (ACN) has a large surface area that provides the basis for an optimized gas exchange in the lung. It needs to adapt to morphological changes during early lung development and alveolarization. Structural alterations of the pulmonary vasculature can lead to pathological functional conditions such as in bronchopulmonary dysplasia and various other lung diseases. To understand the development of the ACN and its impact on the pathogenesis of lung diseases, methods are needed that enable comparative analyses of the complex three-dimensional structure of the ACN at different developmental stages and under pathological conditions. In this study a newborn mouse lung was imaged with serial block-face scanning electron microscopy (SBF-SEM) to investigate the ACN and its surrounding structures before the alveolarization process begins. Most parts but not all of the examined ACN contain two layers of capillaries, which were repeatedly connected with each other. A path from an arteriole to a venule was extracted and straightened to allow cross-sectional visualization of the data along the path within a plane. This allows a qualitative characterization of the structures that erythrocytes pass on their way through the ACN. One way to define regions of the ACN supplied by specific arterioles is presented and used for analyses. Pillars, possibly intussusceptive, were found in the vasculature but no specific pattern was observed in regard to parts of the saccular septa. This study provides 3D information with a resolution of about 150 nm on the microscopic structure of a newborn mouse lung and outlines some of the potentials and challenges of SBF-SEM for 3D analyses of the ACN. },
}

@article{RN845,
  year = { 2019 },
  volume = { 50 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065894779{\&}doi=10.1016{\%}2Fj.jmir.2019.03.184{\&}partnerID=40{\&}md5=710b20f7941f0fb981b12e9164a50e6a },
  type = { Journal Article },
  title = { Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy },
  pages = { 425--440 },
  number = { 3 },
  keywords = { MR-only radiotherapy,Pseudo-CT,brain,hybrid CT number correction,multiatlas fusion },
  journal = { Journal of Medical Imaging and Radiation Sciences },
  issn = { 18767982 },
  doi = { 10.1016/j.jmir.2019.03.184 },
  author = { Boukellouz and Moussaoui and Taleb-Ahmed and Boydev },
  abstract = { Objective: To propose a hybrid multiatlas fusion and correction approach to estimate a pseudo–computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy. Materials and Methods: A set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image. Results: The proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement. Conclusion: A hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images. },
}

@article{RN826,
  year = { 2019 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol },
  pages = { 1453--1467 },
  number = { 5 },
  keywords = { ENIGMA EPI template,Large multi-site studies,Processing pipelines },
  journal = { Brain Imaging and Behavior },
  issn = { 19317565 },
  doi = { 10.1007/s11682-018-9941-x },
  author = { Adhikari and Jahanshad and Shukla and Turner and Grotegerd and Dannlowski and Kugel and Engelen and Dietsche and Krug and Kircher and Fieremans and Veraart and Novikov and Boedhoe and Werf and Heuvel and Ipser and Uhlmann and Stein and Dickie and Voineskos and Malhotra and Pizzagalli and Calhoun and Waller and Veer and Walter and Buchanan and Glahn and Hong and Thompson and Kochunov },
  abstract = { Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations. },
}

@incollection{Pryamonosov2019,
  year = { 2019 },
  volume = { 133 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060769579{\&}doi=10.1007{\%}2F978-3-030-06228-6{\_}26{\&}partnerID=40{\&}md5=2732253554b40b79957430c0178be429 },
  type = { Serial },
  title = { Robustness analysis of coronary arteries segmentation },
  pages = { 331--344 },
  keywords = { Cardiovascular applications,Computed tomography,Contrast enhanced,Coronary arteries,Image segmentation,Personalized medicine },
  issn = { 21903026 },
  isbn = { 9783030062279 },
  doi = { 10.1007/978-3-030-06228-6_26 },
  booktitle = { Smart Innovation, Systems and Technologies },
  author = { Pryamonosov and Danilov },
  abstract = { Segmentation of medical scans is the first and fundamental stage of numerical modeling of the human cardiovascular system. In this chapter, we analyze the results of coronary arteries segmentation using our approach for ten contrast-enhanced Computer Tomography Angiography datasets with different image quality and contrast phases. The segmentation is also affected by the patient anatomy, the shape and the scope of images. Our results show that the contrast phase timing is crucial for successful automatic segmentation. These factors form restrictions on the input data for automatic segmentation algorithms. Nevertheless, user guidance such as manual seeding and setting of thresholds can be used to significantly improve segmentation results and weaken the input restrictions. },
}

@incollection{RN836,
  year = { 2019 },
  volume = { 11764 LNCS },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075630951{\&}doi=10.1007{\%}2F978-3-030-32239-7{\_}86{\&}partnerID=40{\&}md5=e5b88e05907541abbf2b36b425c9f544 },
  type = { Serial },
  title = { Fine-scale vessel extraction in fundus images by registration with fluorescein angiography },
  publisher = { Springer },
  pages = { 779--787 },
  keywords = { Filamentary vessels,Fine-scale vessel segmentation,Fluorescein angiography,Fundus images,Registration },
  issn = { 16113349 },
  isbn = { 9783030322380 },
  editor = { [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-030-32239-7_86 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Noh and Park and Lee },
  abstract = { We present a new framework for fine-scale vessel segmentation from fundus images through registration and segmentation of corresponding fluorescein angiography (FA) images. In FA, fluorescent dye is used to highlight the vessels and increase their contrast. Since these highlights are temporally dispersed among multiple FA frames, we first register the FA frames and aggregate the per-frame segmentations to construct a detailed vessel mask. The constructed FA vessel mask is then registered to the fundus image based on an initial fundus vessel mask. Postprocessing is performed to refine the final vessel mask. Registration of FA frames, as well as registration of FA vessel mask to the fundus image, are done by similar hierarchical coarse-to-fine frameworks, both comprising rigid and non-rigid registration. Two CNNs with identical network structures, both trained on public datasets but with different settings, are used for vessel segmentation. The resulting final vessel segmentation contains fine-scale, filamentary vessels extracted from FA and corresponding to the fundus image. We provide quantitative evaluation as well as qualitative examples which support the robustness and the accuracy of the proposed method. },
}

@inproceedings{AlDhamari,
  year = { 2019 },
  volume = { 2019-April },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073909464{\&}doi=10.1109{\%}2FISBI.2019.8759223{\&}partnerID=40{\&}md5=1c62e6194c979866476e3863b6d3f513 {\%}3CGo to },
  type = { Conference Proceedings },
  title = { Automatic detection of cervical spine ligaments origin and insertion points },
  series = { IEEE International Symposium on Biomedical Imaging },
  publisher = { Ieee },
  pages = { 48--51 },
  keywords = { Acir,Asgd,Atlas-segmentation,Detection,Origin and insertion points,Registration,Spine },
  issn = { 19458452 },
  isbn = { 9781538636411 },
  doi = { 10.1109/ISBI.2019.8759223 },
  booktitle = { Proceedings - International Symposium on Biomedical Imaging },
  author = { Al-Dhamari and Bauer and Keller and Paulus },
  address = { New York },
  abstract = { Creating patient-specific simulation models helps to make customised implant or treatment plans. To create such models, exact locations of the Origin and Insertion Points of the Ligaments (OIPL) are required. Locating these OIPL is usually done manually through a time-consuming procedure.A fast method to detect these OIPL automatically using spine atlas-based segmentation is proposed in this paper. The average detection rate is 96.16{\%} with a standard deviation of 3.45. The required time to detect these points is approximately 5 seconds. The proposed method can be generalised to detect any other important points or features related to a specific vertebra.The method is implemented as an open-source plugin for 3D Slicer. The method and the datasets can be download for free from a public server. },
}

@inproceedings{Weber,
  year = { 2019 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077880308{\&}doi=10.1109{\%}2FEMBC.2019.8856481{\&}partnerID=40{\&}md5=289fafd6a7830c42d4e57271141c9bce },
  type = { Conference Proceedings },
  title = { A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores },
  pages = { 3463--3467 },
  issn = { 1557170X },
  isbn = { 9781538613115 },
  doi = { 10.1109/EMBC.2019.8856481 },
  booktitle = { Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS },
  author = { Weber and Wild and Wallner and Egger },
  abstract = { Image segmentation plays a major role in medical imaging. Especially in radiology, the detection and development of tumors and other diseases can be supported by image segmentation applications. Tools that provide image segmentation and calculation of segmentation scores are not available at any time for every device due to the size and scope of functionalities they offer. These tools need huge periodic updates and do not properly work on old or weak systems. However, medical use-cases often require fast and accurate results. A complex and slow software can lead to additional stress and thus unnecessary errors. The aim of this contribution is the development of a cross-platform tool for medical segmentation use-cases. The goal is a device-independent and always available possibility for medical imaging including manual segmentation and metric calculation. The result is Studierfenster (studierfenster.at), a web-tool for manual segmentation and segmentation metric calculation. In this contribution, the focus lies on the segmentation metric calculation part of the tool. It provides the functionalities of calculating directed and undirected Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) scores for two uploaded volumes, filtering for specific values, searching for specific values in the calculated metrics and exporting filtered metric lists in different file formats. },
}

@inproceedings{Izard,
  year = { 2019 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075443653{\&}doi=10.1145{\%}2F3362789.3362936{\&}partnerID=40{\&}md5=4c4690e277fd6b868cb7be97c738a3ed },
  type = { Conference Proceedings },
  title = { NextMed, Augmented and Virtual Reality platform for 3D medical imaging visualization: Explanation of the software platform developed for 3D models visualization related with medical images using Augmented and Virtual Reality technology },
  pages = { 459--468 },
  keywords = { Augmented Reality,Automatic Segmentation,Medical Imaging,Virtual Reality },
  isbn = { 9781450371919 },
  doi = { 10.1145/3362789.3362936 },
  booktitle = { ACM International Conference Proceeding Series },
  author = { Izard and Plaza and Torres and M{\'{e}}ndez and Garc{\'{i}}a-Pẽalvo },
  abstract = { The visualization of the radiological results with more advanced techniques than the current ones, such as Augmented Reality and Virtual Reality technologies, represent a great advance for medical professionals, by eliminating their imagination capacity as an indispensable requirement for the understanding of medical images. The problem is that for its application it is necessary to segment the anatomical areas of interest, and this currently involves the intervention of the human being. The Nextmed project is presented as a complete solution that includes DICOM images import, automatic segmentation of certain anatomical structures, 3D mesh generation of the segmented area, visualization engine with Augmented Reality and Virtual Reality, all thanks to different software platforms that have been implemented and detailed, including results obtained from real patients. We will focus on the visualization platform using both Augmented and Virtual Reality technologies to allow medical professionals to work with 3d model representation of medical images in a different way taking advantage of new technologies. },
}

@inproceedings{RN855,
  year = { 2019 },
  volume = { 1160 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062482878{\&}doi=10.1088{\%}2F1742-6596{\%}2F1160{\%}2F1{\%}2F012002{\&}partnerID=40{\&}md5=03a70ac3ff75e964350486d67a57b008 },
  type = { Conference Proceedings },
  title = { Segmentation of brain tumors using a semi-automatic computational strategy },
  publisher = { Institute of Physics Publishing },
  number = { 1 },
  issn = { 17426596 },
  isbn = { 17426588 (ISSN) },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.1088/1742-6596/1160/1/012002 },
  booktitle = { Journal of Physics: Conference Series },
  author = { Vera and Hu{\'{e}}rfano and Gelvez and Valbuena and Salazar and Molina and Vera and Salazar and S{\'{a}}enz },
  abstract = { In this work, a semi-automatic computational strategy is proposed for brain tumor segmentation. The filtering (erosion + gaussian filters), segmentation (level set technique) and quantification (BT volume) stages are applied to magnetic resonance imaging in order to generate the three-dimensional morphology of brain tumors. The Jaccard's Similarity Index is considered to contrast manual segmentation with semi-automatic segmentations of brain tumor. In this sense, the highest Jaccard's Similarity Index provides the best parameters of the techniques that constitute the semi-automatic computational strategy. Results are promising, showing an excellent correlation between these segmentations. The volume is used for the brain tumors characterization. },
}

@inproceedings{RN857,
  year = { 2019 },
  volume = { 11155 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078146492{\&}doi=10.1117{\%}2F12.2532730{\&}partnerID=40{\&}md5=9a2bd6db58344dccac47650014f184cb },
  type = { Conference Proceedings },
  title = { Improving band to band registration accuracy of SEVIRI level 1.5 products },
  publisher = { SPIE },
  pages = { 2 },
  issn = { 1996756X },
  isbn = { 9781510630130 },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.1117/12.2532730 },
  booktitle = { Image and Signal Processing for Remote Sensing XXV 2019 },
  author = { Nain and Mueller },
}

@inproceedings{RN858,
  year = { 2019 },
  volume = { 10953 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068414398{\&}doi=10.1117{\%}2F12.2513007{\&}partnerID=40{\&}md5=919e41c5d51d55066df5c354c9c554f4 },
  type = { Conference Proceedings },
  title = { Methods for quantitative characterization of bone injury from computed-tomography images },
  publisher = { SPIE },
  pages = { 40 },
  issn = { 0277-786X },
  isbn = { 9781510625532 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2513007 },
  booktitle = { Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging },
  author = { Hernandez-Cerdan and Paniagua and Prothero and Marron and Livingston and Bateman and McCormick },
}

@inproceedings{RN847,
  year = { 2019 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071158969{\&}doi=10.1145{\%}2F3329714.3338131{\&}partnerID=40{\&}md5=2a6a9a2b43279419c898a8f0a38d80a6 },
  type = { Conference Proceedings },
  title = { Progressive ray-casting volume rendering with WebGL for visual assessment of air void distribution in quality control },
  publisher = { Association for Computing Machinery, Inc },
  keywords = { Industrial application,Progressive rendering,Quality control,Ray-casting,Ubiquitous platforms,Void segmentation,Volume rendering,Web,WebGL },
  isbn = { 9781450367981 },
  editor = { [object Object] },
  doi = { 10.1145/3329714.3338131 },
  booktitle = { Proceedings - Web3D 2019: 24th International ACM Conference on 3D Web Technology },
  author = { Arbelaiz and Moreno and Barandiaran and Garc{\'{i}}a-Alonso },
  abstract = { Due to a lack of ubiquitous tools for volume data visualization, 3D rendering of volumetric content is shared and distributed as 2D media (video and static images). This work shows how using open web technologies (HTML5, JavaScript,WebGL and SVG), high quality volume rendering is achievable in an interactive manner with any WebGL-enabled device. In the web platform, real-time volume rendering algorithms are constrained to small datasets. This work presents a WebGL progressive ray-casting volume rendering approach that allows the interactive visualization of larger datasets with a higher rendering quality. This approach is better suited for devices with low compute capacity such as tablets and mobile devices. As a validation case, the presented method is used in an industrial quality inspection use case to visually assess the air void distribution of a plastic injection mould component in the web browser. },
}

@inbook{Suter2018,
  year = { 2018 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { FAST AND UNCERTAINTY-AWARE CEREBRAL CORTEX MORPHOMETRY ESTIMATION USING RANDOM FOREST REGRESSION },
  series = { IEEE International Symposium on Biomedical Imaging },
  publisher = { Ieee },
  pages = { 1052--1055 },
  isbn = { 978-1-5386-3636-7 },
  booktitle = { 2018 Ieee 15th International Symposium on Biomedical Imaging },
  author = { Suter and Rummel and Wiest and Reyes and Ieee },
  address = { New York },
}

@inbook{Harris2018,
  year = { 2018 },
  volume = { 10580 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Automated registration and stitching of multiple 3D ultrasound images for monitoring neonatal intraventricular hemorrhage },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 42 },
  issn = { 16057422 },
  isbn = { 9781510616493 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2292925 },
  booktitle = { Medical Imaging 2018: Ultrasonic Imaging and Tomography },
  author = { Harris and Kishimoto and Fenster and Ribaupierre and Gardi },
  address = { Bellingham },
  abstract = { {\textcopyright} 2018 SPIE. Dilatation of the cerebral ventricles is a common condition in preterm neonates with intraventricular hemorrhage (IVH). Post Hemorrhagic Ventricular Dilatation (PHVD) can lead to lifelong neurological impairment caused by ischemic injury due to increased intracranial pressure, and without treatment can lead to death. Previously, we have developed and validated a 3D ultrasound (US) system to monitor the progression of ventricle volumes (VV) in IVH patients; however, many patients with severe PHVD have ventricles so large they cannot be imaged within a single 3D US image. This limits the utility of atlas based segmentation algorithms required to measure VV as parts of the ventricles are in separate 3D US images, and thus, an already challenging segmentation becomes increasingly difficult to solve. Without a more automated segmentation, the clinical utility of 3D US ventricle volumes cannot be fully realized due to the large number of images and patients required to validate the technique in a clinical trials. Here, we describe the initial results of an automated {\^{a}}stitching' algorithm used to register and combine multiple 3D US images of the ventricles of patients with PHVD. Our registration results show that we were able to register these images with an average target registration error (TRE) of 4.25±1.95 mm. },
}

@inbook{Santos2018,
  year = { 2018 },
  volume = { 10917 LNCS },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { A Proposal for Combining Ultrasound, Magnetic Resonance Imaging and Force Feedback Technology, During the Pregnancy, to Physically Feel the Fetus },
  series = { Lecture Notes in Computer Science },
  publisher = { Springer International Publishing Ag },
  pages = { 502--512 },
  keywords = { Fetus,Haptics,Interaction,MRI,Ultrasound },
  issn = { 16113349 },
  isbn = { 9783319913964 },
  editor = { [object Object] },
  doi = { 10.1007/978-3-319-91397-1_40 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Santos} and Werner and Raposo and Hurtado and Arcoverde and Ribeiro },
  address = { Cham },
  abstract = { Evolutions in image-scanning technology have led to vast improvements in the fetal assessment. Ultrasound (US) is the main technology for fetal evaluation. Magnetic resonance imaging (MRI) is generally used when US cannot provide high-quality images. This paper presents an interactive bidirectional actuated human-machine interface proposal developed by the combination of a haptic device system (force-feedback technology) and a non-invasive medical image technology. },
}

@inbook{RN822,
  year = { 2018 },
  volume = { 11037 LNCS },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Lung nodule synthesis using cnn-based latent data representation },
  series = { Lecture Notes in Computer Science },
  publisher = { Springer International Publishing Ag },
  pages = { 111--118 },
  keywords = { Convolutional neural networks,Generative models,Lung nodule false positive reduction,Multivariate Gaussian mixture models,Nodules synthesis },
  issn = { 16113349 },
  isbn = { 9783030005351 },
  editor = { [object Object],[object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-030-00536-8_12 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Oliveira and Viana },
  address = { Cham },
  abstract = { Convolutional neural networks (CNNs) have been widely used to address various image analysis problems at the cost of intensive computational load and large amounts of annotated training data. When it comes to Medical Imaging, annotation is often complicated and/or expensive, and innovative methods for dealing with small or very imbalanced training sets are mostly welcome. In this context, this paper proposes a novel approach for efficiently synthesizing volumetric patch data from a small amount of samples using their latent data. Our method consists of two major steps. First, we train a 3D CNN auto-encoder for unsupervised learning of volumetric latent data by means of multivariate Gaussian mixture models (GMMs): while the encoder finds latent representations of volumes using GMMs, the decoder uses the estimated GMMs parameters to reconstruct the volume observed in the input. Then, we modify latent data of samples at training time to generate similar, but different, new samples: we run non-rigid registrations between patches decoded from real latent data and patches decoded from modified latent data, and warp the corresponding original image patches using the resulting displacement fields. We evaluated our method in the context of lung nodules synthesis using the publicly available LUNA challenge dataset, and generated new realistic samples out of real lung nodules, preserving their original texture and neighbouring anatomical structures. Our results demonstrate that 3D CNNs trained using our synthesis method were able to consistently deliver lower lung nodule false positive rates, which indicates an improvement in the networks discriminant power. },
}

@article{yaniv2018simpleitk,
  year = { 2018 },
  volume = { 31 },
  title = { SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research },
  publisher = { Springer International Publishing },
  pages = { 290--303 },
  number = { 3 },
  journal = { Journal of digital imaging },
  author = { Yaniv and Lowekamp and Johnson and Beare },
}

@article{yaniv2018simpleitk,
  year = { 2018 },
  volume = { 31 },
  title = { SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research },
  publisher = { Springer International Publishing },
  pmid = { 29181613 },
  pages = { 290--303 },
  number = { 3 },
  keywords = { Image analysis,Open-source software,Python,R,Registration,Segmentation },
  journal = { Journal of Digital Imaging },
  issn = { 1618727X },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Yaniv et al/Journal of Digital Imaging/Yaniv et al. - 2018 - SimpleITK Image-Analysis Notebooks a Collaborative Environment for Education and Reproducible Research.pdf:pdf },
  doi = { 10.1007/s10278-017-0037-8 },
  author = { Yaniv and Lowekamp and Johnson and Beare },
  abstract = { Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks. },
}

@article{Nyholm2018,
  year = { 2018 },
  volume = { 45 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project: },
  pages = { 1295--1300 },
  number = { 3 },
  keywords = { CT,MRI,open dataset,organs at risk,radiotherapy },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.12748 },
  author = { Nyholm and Svensson and Andersson and Jonsson and Sohlin and Gustafsson and Kjell{\'{e}}n and S{\"{o}}derstr{\"{o}}m and Albertsson and Blomqvist and Zackrisson and Olsson and Gunnlaugsson },
  abstract = { Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). Acquisition and validation methods: T1-and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. Data format and usage notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. Potential applications: The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm. },
}

@article{Zhang2018,
  year = { 2018 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051860976{\&}doi=10.1007{\%}2Fs12350-018-1392-7{\&}partnerID=40{\&}md5=58fd21a00fccc01830e89c4949b3bcee },
  type = { Journal Article },
  title = { Evaluation of different respiratory gating schemes for cardiac SPECT },
  keywords = { Cardiac perfusion,Respiratory gating,SPECT/CT,Simulation },
  journal = { Journal of Nuclear Cardiology },
  issn = { 15326551 },
  doi = { 10.1007/s12350-018-1392-7 },
  author = { Zhang and Pretorius and Ghaly and Zhang and King and Mok },
  abstract = { Background: Respiratory gating reduces motion blurring in cardiac SPECT. Here we aim to evaluate the performance of three respiratory gating strategies using a population of digital phantoms with known truth and clinical data. Methods: We analytically simulated 60 projections for 10 XCAT phantoms with 99mTc-sestamibi distributions using three gating schemes: equal amplitude gating (AG), equal count gating (CG), and equal time gating (TG). Clinical list-mode data for 10 patients who underwent 99mTc-sestamibi scans were also processed using the 3 gating schemes. Reconstructed images in each gate were registered to a reference gate, averaged and reoriented to generate the polar plots. For simulations, image noise, relative difference (RD) of averaged count for each of the 17 segment, and relative defect size difference (RSD) were analyzed. For clinical data, image intensity profile and FWHM were measured across the left ventricle wall. Results: For simulations, AG and CG methods showed significantly lower RD and RSD compared to TG, while noise variation was more non-uniform through different gates for AG. In the clinical study, AG and CG had smaller FWHM than TG. Conclusions: AG and CG methods show better performance for motion reduction and are recommended for clinical respiratory gating SPECT implementation. },
}

@article{Vijayavenkataraman2018,
  year = { 2018 },
  volume = { 132 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { 3D bioprinting of tissues and organs for regenerative medicine },
  pages = { 296--332 },
  keywords = { 3D bioprinting,3D printing,Bioprinting,Organ printing,Regenerative medicine,Tissue engineering },
  journal = { Advanced Drug Delivery Reviews },
  issn = { 18728294 },
  doi = { 10.1016/j.addr.2018.07.004 },
  author = { Vijayavenkataraman and Yan and Lu and Wang and Fuh },
  abstract = { 3D bioprinting is a pioneering technology that enables fabrication of biomimetic, multiscale, multi-cellular tissues with highly complex tissue microenvironment, intricate cytoarchitecture, structure-function hierarchy, and tissue-specific compositional and mechanical heterogeneity. Given the huge demand for organ transplantation, coupled with limited organ donors, bioprinting is a potential technology that could solve this crisis of organ shortage by fabrication of fully-functional whole organs. Though organ bioprinting is a far-fetched goal, there has been a considerable and commendable progress in the field of bioprinting that could be used as transplantable tissues in regenerative medicine. This paper presents a first-time review of 3D bioprinting in regenerative medicine, where the current status and contemporary issues of 3D bioprinting pertaining to the eleven organ systems of the human body including skeletal, muscular, nervous, lymphatic, endocrine, reproductive, integumentary, respiratory, digestive, urinary, and circulatory systems were critically reviewed. The implications of 3D bioprinting in drug discovery, development, and delivery systems are also briefly discussed, in terms of in vitro drug testing models, and personalized medicine. While there is a substantial progress in the field of bioprinting in the recent past, there is still a long way to go to fully realize the translational potential of this technology. Computational studies for study of tissue growth or tissue fusion post-printing, improving the scalability of this technology to fabricate human-scale tissues, development of hybrid systems with integration of different bioprinting modalities, formulation of new bioinks with tuneable mechanical and rheological properties, mechanobiological studies on cell-bioink interaction, 4D bioprinting with smart (stimuli-responsive) hydrogels, and addressing the ethical, social, and regulatory issues concerning bioprinting are potential futuristic focus areas that would aid in successful clinical translation of this technology. },
}

@article{Shams2018,
  year = { 2018 },
  volume = { 37 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030780937{\&}doi=10.1109{\%}2FTMI.2017.2755695{\&}partnerID=40{\&}md5=c0b386f770628888db0e4e521b9905ba },
  type = { Journal Article },
  title = { Assessment of Rigid Registration Quality Measures in Ultrasound-Guided Radiotherapy },
  pages = { 428--437 },
  number = { 2 },
  keywords = { Radiotherapy,bootstrapping,image registration,motion management,quality management,supervised learning },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 1558254X },
  doi = { 10.1109/TMI.2017.2755695 },
  author = { Shams and Xiao and Hebert and Abramowitz and Brooks and Rivaz },
  abstract = { Image guidance has become the standard of care for patient positioning in radiotherapy, where image registration is often a critical step to help manage patient motion. However, in practice, verification of registration quality is often adversely affected by difficulty in manual inspection of 3-D images and time constraint, thus affecting the therapeutic outcome. Therefore, we proposed to employ both bootstrapping and the supervised learning methods of linear discriminant analysis and random forest to help robustly assess registration quality in ultrasound-guided radiotherapy. We validated both approaches using phantom and real clinical ultrasound images, and showed that both performed well for the task. While learning-based techniques offer better accuracy and shorter evaluation time, bootstrapping requires no prior training and has a higher sensitivity. },
}

@article{Ramirez2018,
  year = { 2018 },
  volume = { 26 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Co-creation and open innovation: Systematic literature review },
  pages = { 9--18 },
  number = { 54 },
  keywords = { Citizen science,Collaboration,Innovation,Knowledge,Knowledge co-creation,Open science,Openness,Validation },
  journal = { Comunicar },
  issn = { 19883293 },
  doi = { 10.3916/C54-2018-01 },
  author = { Ram{\'{i}}rez and Garc{\'{i}}a-Pe{\~{n}}alvo },
  abstract = { Open science, as a common good, opens possibilities for the development of nations, through innovations and collaborative constructions, which help to democratize knowledge. Advances in this area are still emerging, and the open science, co-creation of knowledge and open innovation triangle, is presented as an opportunity to generate an original contribution from research to open educational theory and practices. The study analyzed the articles that addressed this triangle, in order to identify the contexts and challenges that arise in open innovation and the co-creation of knowledge to promote open science. The method was a systematic literature review (SLR) of 168 articles published in open access format, from January 2014 to May 2017 in the Web of Science and Scopus databases. In the validation process, the York University criteria were used: inclusion and exclusion, relevance of the pertinent studies, evaluation of the quality / validity of included studies and description of data / basic studies. The findings showed that the most-widely publicized contexts were in the United States and Brazil, in the business and academic sectors (closely followed by the social sector), and the challenges were open to innovation, opening and research. The research concludes that the context and practices of collaboration are substantial elements for innovation and open science. },
}

@article{Prasetio2018,
  year = { 2018 },
  volume = { 63 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Dose calculation and verification of the Vero gimbal tracking treatment delivery },
  pages = { 16 },
  number = { 3 },
  keywords = { MU distribution,dose reconstruction,dynamic tumor tracking,film dosimetry,gimbal tracking },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/aaa617 },
  author = { Prasetio and W{\"{o}}lfelschneider and Ziegler and Serpa and Witulla and Bert },
  abstract = { The Vero linear accelerator delivers dynamic tumor tracking (DTT) treatment using a gimbal motion. However, the availability of treatment planning systems (TPS) to simulate DTT is limited. This study aims to implement and verify the gimbal tracking beam geometry in the dose calculation. Gimbal tracking was implemented by rotating the reference CT outside the TPS according to the ring, gantry, and gimbal tracking position obtained from the tracking log file. The dose was calculated using these rotated CTs. The geometric accuracy was verified by comparing calculated and measured film response using a ball bearing phantom. The dose was verified by comparing calculated 2D dose distributions and film measurements in a ball bearing and a homogeneous phantom using a gamma criterion of 2{\%}/2 mm. The effect of implementing the gimbal tracking beam geometry in a 3D patient data dose calculation was evaluated using dose volume histograms (DVH). Geometrically, the gimbal tracking implementation accuracy was {\textless}0.94 mm. The isodose lines agreed with the film measurement. The largest dose difference of 9.4{\%} was observed at maximum tilt positions with an isocenter and target separation of 17.51 mm. Dosimetrically, gamma passing rates were {\textgreater}98.4{\%}. The introduction of the gimbal tracking beam geometry in the dose calculation shifted the DVH curves by 0.05{\%}-1.26{\%} for the phantom geometry and by 5.59{\%} for the patient CT dataset. This study successfully demonstrates a method to incorporate the gimbal tracking beam geometry into dose calculations. By combining CT rotation and MU distribution according to the log file, the TPS was able to simulate the Vero tracking treatment dose delivery. The DVH analysis from the gimbal tracking dose calculation revealed changes in the dose distribution during gimbal DTT that are not visible with static dose calculations. },
}

@article{Phellan2018,
  year = { 2018 },
  volume = { 65 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images },
  pages = { 1486--1494 },
  number = { 7 },
  keywords = { Angiography,magnetic resonance angiography,temporal segmentation,vessel enhancement,vessel segmentation },
  journal = { IEEE Transactions on Biomedical Engineering },
  issn = { 15582531 },
  doi = { 10.1109/TBME.2017.2759730 },
  author = { Phellan and Lindner and Helle and Falcao and Forkert },
  abstract = { Objective: Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a separate segmentation of each image. This paper introduces a method that combines both approaches to overcome the specific drawbacks of each technique. Methods: Vessels in the tAIP are enhanced by using the ranking orientation responses of path operators and multiscale vesselness enhancement filters. Then, tAIP segmentation is performed using a seed-based algorithm. In parallel, this algorithm is also used to segment each frame of the series and identify small vessels, which might have been lost in the tAIP segmentation. The results of each individual time frame segmentation are fused using an or boolean operation. Finally, small vessels found only in the fused segmentation are added to the tAIP segmentation. Results: In a quantitative analysis using ten 4D ASL MRA image series from healthy volunteers, the proposed combined approach reached an average Dice coefficient of 0.931, being more accurate than the corresponding tMIP, tAIP, and single time frame segmentation methods with statistical significance. Conclusion : The novel combined vessel segmentation strategy can be used to obtain improved vessel segmentation results from 4D ASL MRA and other dynamic series of images. Significance: Improved vessel segmentation of 4D ASL MRA allows a fast and accurate assessment of cerebrovascular structures. },
}

@article{Patterson2018,
  year = { 2018 },
  volume = { 90 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy },
  pages = { 12404--12413 },
  number = { 21 },
  journal = { Analytical Chemistry },
  issn = { 15206882 },
  doi = { 10.1021/acs.analchem.8b02885 },
  author = { Patterson and Tuck and Lewis and Kaushansky and Norris and {Van De Plas} and Caprioli },
  abstract = { Histology-directed imaging mass spectrometry (IMS) is a spatially targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations onto the IMS tissue section. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational monomodal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (-3-10 $\mu$m based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1{\%} of the tissue section area) malaria infected mouse hepatocytes and glomeruli in the human kidney case study. },
}

@article{Kostenko2018,
  year = { 2018 },
  volume = { 26 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Registration-based multi-orientation tomography },
  pages = { 28982 },
  number = { 22 },
  journal = { Optics Express },
  issn = { 1094-4087 },
  doi = { 10.1364/oe.26.028982 },
  author = { Kostenko and Andriiashen and Batenburg },
  abstract = { We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and experiments show that the solutions computed using algebraic methods based on multiple acquisitions can achieve a dramatic improvement in the reconstruction quality, even when each acquisition generates a reduced number of projections. The approach does not require any advanced setup components making it ideal for laboratory-based X-ray tomography. },
}

@article{Joshi2018,
  year = { 2018 },
  volume = { 4 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Iterative peak combination: A robust technique for identifying relevant features in medical image histograms },
  pages = { 10 },
  number = { 1 },
  keywords = { cone beam CT,histogram matching,image histogram,image processing },
  journal = { Biomedical Physics and Engineering Express },
  issn = { 20571976 },
  doi = { 10.1088/2057-1976/aa929d },
  author = { Joshi and Marchant },
  abstract = { Histogram-based methods can be used to analyse and transform medical images. Histogram specification is one such method which has been widely used to transform the histograms of cone beam CT (CBCT) images to match those of corresponding CT images. However, when the derived transformation is applied to the CBCT image pixels, significant artefacts can be produced. We propose the iterative peak combination algorithm, a novel and robust method for automatically identifying relevant features in medical image histograms. The procedure is conceptually simple and can be applied equally well to both CT and CBCT image histograms. We also demonstrate how iterative peak combination can be used to transform CBCT images in such as way as to improve the Hounsfield Unit (HU) calibration of CBCT image pixel values, without introducing additional artefacts. We analyse 36 pelvis CBCT images and show that the average difference in fat tissue pixel values between CT images and CBCT images processed using the iterative peak combination algorithm is 23.7 HU. Compared to 136.7 HU in unprocessed CBCT images and 50.9 in CBCT images processed using histogram specification. },
}

@article{Ho2018,
  year = { 2018 },
  volume = { 2018 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Estimation of the Craniectomy Surface Area by Using Postoperative Images },
  pages = { 8 },
  journal = { International Journal of Biomedical Imaging },
  issn = { 16874196 },
  doi = { 10.1155/2018/5237693 },
  author = { Ho and Tseng and Xiao },
  abstract = { Decompressive craniectomy (DC) is a neurosurgical procedure performed to relieve the intracranial pressure engendered by brain swelling. However, no easy and accurate method exists for determining the craniectomy surface area. In this study, we implemented and compared three methods of estimating the craniectomy surface area for evaluating the decompressive effort. We collected 118 sets of preoperative and postoperative brain computed tomography images from patients who underwent craniectomy procedures between April 2009 and April 2011. The surface area associated with each craniectomy was estimated using the marching cube and quasi-Monte Carlo methods. The surface area was also estimated using a simple AC method, in which the area is calculated by multiplying the craniectomy length (A) by its height (C). The estimated surface area ranged from 9.46 to 205.32 cm2, with a median of 134.80 cm2. The root-mean-square deviation (RMSD) between the marching cube and quasi-Monte Carlo methods was 7.53 cm2. Furthermore, the RMSD was 14.45 cm2 between the marching cube and AC methods and 12.70 cm2 between the quasi-Monte Carlo and AC methods. Paired t-tests indicated no statistically significant difference between these methods. The marching cube and quasi-Monte Carlo methods yield similar results. The results calculated using the AC method are also clinically acceptable for estimating the DC surface area. Our results can facilitate additional studies on the association of decompressive effort with the effect of craniectomy. },
}

@article{Han2018,
  year = { 2018 },
  volume = { 63 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A momentum-based diffeomorphic demons framework for deformable MR-CT image registration },
  pmid = { 30353886 },
  pages = { 18 },
  number = { 21 },
  keywords = { deformable registration,image registration,neurosurgery navigation },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/aae66c },
  author = { Han and {De Silva} and Ketcha and Uneri and Siewerdsen },
  abstract = { Neuro-navigated procedures require a high degree of geometric accuracy but are subject to geometric error from complex deformation in the deep brain - e.g. regions about the ventricles due to egress of cerebrospinal fluid (CSF) upon neuroendoscopic approach or placement of a ventricular shunt. We report a multi-modality, diffeomorphic, deformable registration method using momentum-based acceleration of the Demons algorithm to solve the transformation relating preoperative MRI and intraoperative CT as a basis for high-precision guidance. The registration method (pMI-Demons) extends the mono-modality, diffeomorphic form of the Demons algorithm to multi-modality registration using pointwise mutual information (pMI) as a similarity metric. The method incorporates a preprocessing step to nonlinearly stretch CT image values and incorporates a momentum-based approach to accelerate convergence. Registration performance was evaluated in phantom and patient images: first, the sensitivity of performance to algorithm parameter selection (including update and displacement field smoothing, histogram stretch, and the momentum term) was analyzed in a phantom study over a range of simulated deformations; and second, the algorithm was applied to registration of MR and CT images for four patients undergoing minimally invasive neurosurgery. Performance was compared to two previously reported methods (free-form deformation using mutual information (MI-FFD) and symmetric normalization using mutual information (MI-SyN)) in terms of target registration error (TRE), Jacobian determinant (J), and runtime. The phantom study identified optimal or nominal settings of algorithm parameters for translation to clinical studies. In the phantom study, the pMI-Demons method achieved comparable registration accuracy to the reference methods and strongly reduced outliers in TRE (p 0.001 in Kolmogorov-Smirnov test). Similarly, in the clinical study: median TRE = 1.54 mm (0.83-1.66 mm interquartile range, IQR) for pMI-Demons compared to 1.40 mm (1.02-1.67 mm IQR) for MI-FFD and 1.64 mm (0.90-1.92 mm IQR) for MI-SyN. The pMI-Demons and MI-SyN methods yielded diffeomorphic transformations (J {\textgreater} 0) that preserved topology, whereas MI-FFD yielded unrealistic (J {\textless} 0) deformations subject to tissue folding and tearing. Momentum-based acceleration gave a ∼35{\%} speedup of the pMI-Demons method, providing registration runtime of 10.5 min (reduced to 2.2 min on GPU), compared to 15.5 min for MI-FFD and 34.7 min for MI-SyN. The pMI-Demons method achieved registration accuracy comparable to MI-FFD and MI-SyN, maintained diffeomorphic transformation similar to MI-SyN, and accelerated runtime in a manner that facilitates translation to image-guided neurosurgery. },
}

@article{DeSchryver2018,
  year = { 2018 },
  volume = { 8 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes },
  pages = { 10 },
  number = { 1 },
  journal = { Scientific Reports },
  issn = { 20452322 },
  doi = { 10.1038/s41598-018-25916-5 },
  author = { {De Schryver} and Dierick and Heyndrickx and {Van Stappen} and Boone and {Van Hoorebeke} and Boone },
  abstract = { This work presents a framework to exploit the synergy between Digital Volume Correlation (DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-$\mu$CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360° rotation. },
}

@article{Danelakis2018,
  year = { 2018 },
  volume = { 70 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging },
  pages = { 83--100 },
  keywords = { Automated segmentation,Brain MRI,Multiple sclerosis,Survey },
  journal = { Computerized Medical Imaging and Graphics },
  issn = { 18790771 },
  doi = { 10.1016/j.compmedimag.2018.10.002 },
  author = { Danelakis and Theoharis and Verganelakis },
  abstract = { Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to detect, characterize and quantify MS lesions in the brain, due to the detailed structural information that it can provide. Manual detection and measurement of MS lesions in MRI data is time-consuming, subjective and prone to errors. Therefore, multiple automated methodologies for MRI-based MS lesion segmentation have been proposed. Here, a review of the state-of-the-art of automatic methods available in the literature is presented. The current survey provides a categorization of the methodologies in existence in terms of their input data handling, their main strategy of segmentation and their type of supervision. The strengths and weaknesses of each category are analyzed and explicitly discussed. The positive and negative aspects of the methods are highlighted, pointing out the future trends and, thus, leading to possible promising directions for future research. In addition, a further clustering of the methods, based on the databases used for their evaluation, is provided. The aforementioned clustering achieves a reliable comparison among methods evaluated on the same databases. Despite the large number of methods that have emerged in the field, there is as yet no commonly accepted methodology that has been established in clinical practice. Future challenges such as the simultaneous exploitation of more sophisticated MRI protocols and the hybridization of the most promising methods are expected to further improve the performance of the segmentation. },
}

@article{Caruso2018,
  year = { 2018 },
  volume = { 123 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033363764{\&}doi=10.1007{\%}2Fs11547-017-0833-8{\&}partnerID=40{\&}md5=507a4f33003fadbdd5ac25f4d11e4428 },
  type = { Journal Article },
  title = { Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study },
  pages = { 161--167 },
  number = { 3 },
  keywords = { Colorectal cancer,Haralick's texture analysis,Response to therapy,T2-weighted MRI },
  journal = { Radiologia Medica },
  issn = { 18266983 },
  doi = { 10.1007/s11547-017-0833-8 },
  author = { Caruso and Zerunian and Ciolina and Santis and Rengo and Soomro and Giunta and Conforto and Schmid and Neri and Laghi },
  abstract = { Purpose: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. Materials and Methods: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. Results: Pretreatment MRI examination showed significant value (p {\textless} 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. Conclusions: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients. },
}

@article{Malkasian2018,
  year = { 2018 },
  volume = { 12 },
  title = { Quantification of vessel-specific coronary perfusion territories using minimum-cost path assignment and computed tomography angiography: Validation in a swine model },
  publisher = { Elsevier Inc. },
  pages = { 425--435 },
  number = { 5 },
  month = { sep },
  keywords = { Angiography,Cardiovascular disease,Computerized tomography,Coronary artery disease,Imaging,Myocardium },
  journal = { Journal of Cardiovascular Computed Tomography },
  issn = { 1876861X },
  doi = { 10.1016/j.jcct.2018.06.006 },
  author = { Malkasian and Hubbard and Dertli and Kwon and Molloi },
  abstract = { Background: As combined morphological and physiological assessment of coronary artery disease (CAD) is necessary to reliably resolve CAD severity, the objective of this study was to validate an automated minimum-cost path assignment (MCP) technique which enables accurate, vessel-specific assignment of the left (LCA) and right (RCA) coronary perfusion territories using computed tomography (CT) angiography data for both left and right ventricles. Methods: Six swine were used to validate the MCP technique. In each swine, a dynamic acquisition comprised of twenty consecutive volume scans was acquired with a 320-slice CT scanner following peripheral injection of contrast material. From this acquisition the MCP technique was used to automatically assign LCA and RCA perfusion territories for the left and right ventricles, independently. Each animal underwent another dynamic CT acquisition following direct injection of contrast material into the LCA or RCA. Using this acquisition, reference standard LCA and RCA perfusion territories were isolated from the myocardial blush. The accuracy of the MCP technique was evaluated by quantitatively comparing the MCP-derived LCA and RCA perfusion territories to these reference standard territories. Results: All MCP perfusion territory masses (MassMCP) and all reference standard perfusion territory masses (MassRS) in the left ventricle were related by MassMCP = 0.99MassRS+0.35 g (r = 1.00). MassMCP and MassRS in the right ventricle were related by MassMCP = 0.94MassRS+0.39 g (r = 0.96). Conclusion: The MCP technique was validated in a swine animal model and has the potential to be used for accurate, vessel-specific assignment of LCA and RCA perfusion territories in both the left and right ventricular myocardium using CT angiography data. In order to provide a comprehensive morphological and physiological cardiac imaging datasets, the minimum-cost path assignment (MCP) method has been validated, using a swine animal model and computed tomography (CT) imaging. MCP has been shown to accurately quantify left and right coronary artery perfusion territories in the left ventricle, right ventricle and whole heart. The MCP technique provides clinicians and researchers in cardiovascular imaging with a means to accurately and automatically determine coronary-specific perfusion territories in the left and right heart, and could also be used to assess myocardium at-risk distal to a stenosis. },
}

@article{Oliveira2018a,
  year = { 2018 },
  volume = { 65 },
  title = { Can 11 C-PiB-PET relative delivery R 1 or 11 C-PiB-PET perfusion replace 18 F-FDG-PET in the assessment of brain neurodegeneration? },
  publisher = { IOS Press },
  pages = { 89--97 },
  number = { 1 },
  keywords = { 11 C-PIB,18 F-FDG,Alzheimer's disease,compartmental models,neurodegeneration,perfusion },
  journal = { Journal of Alzheimer's Disease },
  issn = { 18758908 },
  doi = { 10.3233/JAD-180274 },
  author = { Oliveira and Moreira and {De Mendon{\c{c}}a} and Verdelho and Xavier and Barroca and Rio and Cardoso and Cruz and Abrunhosa and Castelo-Branco },
  abstract = { Background: Pittsburgh Compound B (PiB) positron emission tomography (PET) is used to visualize in vivo amyloid plaques in the brain. Frequently the PiB examinations are complemented with a fluorodeoxyglucose (FDG) PET scan to further assess neurodegeneration. Objective: Our goal is to identify alternative correlates of FDG images by assessing which kinetic methods originate PiB derived relative delivery ratio (R 1) images that can be correlated with the FDG images, and to compare them with PiB perfusion (pPiB) images obtained from the early-phase of PiB acquisition. Methods: We selected 52 patients with cognitive impairment who underwent a dynamic PiB and FDG acquisitions. To compute the R 1 images, two simplified reference tissue models (SRTM and SRTM2) and two multi-linear reference tissue models (MRTM and MRTM2) were used. The pPiB images were obtained in two different time intervals. Results: All six types of images were of good quality and highly correlated with the FDG images (mean voxelwise within-subjects r {\textgreater} 0.92). The higher correlation was found for FDG-R 1 (MRTM). Regarding the voxelwise regional correlation, the higher mean all brain correlations was r = 0.825 for FDG-R 1 (MRTM) and statistically significant in the whole brain analysis. Conclusion: All R 1 and pPiB images here tested have potential to assess the metabolic impact of neurodegeneration almost as reliably as the FDG images. However, this is not enough to validate these images for a single-subject analysis compared with the FDG image, and thus they cannot yet be used clinically to replace the FDG image before such evaluation. },
}

@article{Looney2018,
  year = { 2018 },
  volume = { 3 },
  title = { Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning },
  publisher = { NLM (Medline) },
  number = { 11 },
  month = { jun },
  keywords = { Diagnostic imaging,Obstetrics/gynecology,Reproductive Biology },
  journal = { JCI insight },
  issn = { 23793708 },
  doi = { 10.1172/jci.insight.120178 },
  author = { Looney and Stevenson and Nicolaides and Plasencia and Molloholli and Natsis and Collins },
  abstract = { We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA. },
}

@article{RN820,
  year = { 2018 },
  volume = { 63 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases? },
  pages = { 9 },
  number = { 12 },
  keywords = { atlas selection,convolutional neural network,medical image segmentation,multi atlas based segmentation,oracle selection },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/aac712 },
  author = { Zaffino and Ciardo and Raudaschl and Fritscher and Ricotti and Alterio and Marvaso and Fodor and Baroni and Amato and Orecchia and Jereczek-Fossa and Sharp and Spadea },
  abstract = { Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split into two groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle selection, where atlases are chosen by the highest dice similarity coefficient (DSC) with the target, was performed. This was compared to oracle group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, convolutional neural networks were designed to predict the best group of atlases. The results were also compared with the selection strategy based on normalized mutual information (NMI). Oracle group was proven to be significantly better than classic oracle selection (p {\textless} 10-5). Atlas group selection led to a median ± interquartile DSC of 0.740 ± 0.084, 0.718 ± 0.086 and 0.670 ± 0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676 ± 0.113, 0.632 ± 0.104 and 0.606 ± 0.118 (p {\textless} 0.001) as well as classic oracle selection. The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS. },
}

@article{RN867,
  year = { 2018 },
  volume = { 59 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053206243{\&}doi=10.2967{\%}2Fjnumed.117.205286{\&}partnerID=40{\&}md5=2ee14ea7a0f6a85f10442957bf4c6b2e },
  type = { Journal Article },
  title = { Patient-specific computational model and dosimetry calculations for PET/CT of a patient pregnant with twins },
  pages = { 1451--1458 },
  number = { 9 },
  keywords = { CT,Fetus,Monte Carlo simulation,Pregnant female models,Radiation dosimetry },
  journal = { Journal of Nuclear Medicine },
  issn = { 2159662X },
  doi = { 10.2967/jnumed.117.205286 },
  author = { Xie and Zanotti-Fregonara and Edet-Sanson and Zaidi },
  abstract = { The radiation dose delivered to pregnant patients during radiologic imaging procedures raises health concerns because the developing embryo and fetus are considered to be highly radiosensitive. To appropriately weigh the diagnostic benefits against the radiation risks, the radiologist needs reasonably accurate and detailed estimates of the fetal dose. Expanding our previously developed series of computational phantoms for pregnant women, we here describe a personalized model for twin pregnancy, based on an actual clinical scan. Methods: The model is based on a standardized hybrid pregnant female and fetus phantom and on a clinical case of a patient who underwent an 18F-FDG PET/CT scan while expecting twins at 25 weeks' gestation. This model enabled us to produce a realistic physical representation of the pregnant patient and to estimate the maternal and fetal organ doses from the 18FFDG and CT components. The Monte Carlo N-Particle Extended general-purpose code was used for radiation transport simulation. Results: The 18F-FDG doses for the 2 fetuses were 3.78 and 3.99 mGy, and the CT doses were 0.76 and 0.70 mGy, respectively. Therefore, the relative contribution of 18F-FDG and CT to the total dose to the fetuses was about 84{\%} and 16{\%}, respectively. Meanwhile, for 18F-FDG, the calculated personalized absorbed dose was about 40{\%}-50{\%} higher than the doses reported by other dosimetry computer software tools. Conclusion: Our approach to constructing personalized computational models allows estimation of a patient- specific radiation dose, even in cases with unusual anatomic features such as a twin pregnancy. Our results also show that, even in twins, the fetal organ doses from both 18F-FDG and CT present a certain variability linked to the anatomic characteristics. The CT fetal dose is smaller than the 18F-FDG PET dose. },
}

@article{RN889,
  year = { 2018 },
  volume = { 184 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027830512{\&}doi=10.1016{\%}2Fj.ultramic.2017.07.017{\&}partnerID=40{\&}md5=ec5c5bcb14a5795c82946322610e647d },
  type = { Journal Article },
  title = { Improving microstructural quantification in FIB/SEM nanotomography },
  pages = { 24--38 },
  keywords = { 3D reconstruction,Focused ion beam,Microstructure quantification,Scanning electron microscopy,Tortuosity,Triple phase boundaries },
  journal = { Ultramicroscopy },
  issn = { 18792723 },
  doi = { 10.1016/j.ultramic.2017.07.017 },
  author = { Taillon and Pellegrinelli and Huang and Wachsman and Salamanca-Riba },
  abstract = { FIB/SEM nanotomography (FIB-nt) is a powerful technique for the determination and quantification of the three-dimensional microstructure in subsurface features. Often times, the microstructure of a sample is the ultimate determiner of the overall performance of a system, and a detailed understanding of its properties is crucial in advancing the materials engineering of a resulting device. While the FIB-nt technique has developed significantly in the 15 years since its introduction, advanced nanotomographic analysis is still far from routine, and a number of challenges remain in data acquisition and post-processing. In this work, we present a number of techniques to improve the quality of the acquired data, together with easy-to-implement methods to obtain “advanced” microstructural quantifications. The techniques are applied to a solid oxide fuel cell cathode of interest to the electrochemistry community, but the methodologies are easily adaptable to a wide range of material systems. Finally, results from an analyzed sample are presented as a practical example of how these techniques can be implemented. },
}

@article{RN787,
  year = { 2018 },
  volume = { 33 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { 2D-3D registration for 3D analysis of lower limb alignment in a weight-bearing condition },
  pages = { 59--70 },
  number = { 1 },
  keywords = { 2D-3D registration,3D analysis,CT,X-ray,simulated annealing },
  journal = { Applied Mathematics },
  issn = { 10051031 },
  doi = { 10.1007/s11766-018-3459-2 },
  author = { Shim and Kim and Lee and Lee and Woo and Lee },
  abstract = { X-ray imaging is the conventional method for diagnosing the orthopedic condition of a patient. Computerized Tomography(CT) scanning is another diagnostic method that provides patient's 3D anatomical information. However, both methods have limitations when diagnosing the whole leg; X-ray imaging does not provide 3D information, and normal CT scanning cannot be performed with a standing posture. Obtaining 3D data regarding the whole leg in a standing posture is clinically important because it enables 3D analysis in the weight bearing condition. Based on these clinical needs, a hardware-based bi-plane X-ray imaging system has been developed; it uses two orthogonal X-ray images. However, such methods have not been made available in general clinics because of the hight cost. Therefore, we proposed a widely adaptive method for 2D X-ray image and 3D CT scan data. By this method, it is possible to threedimensionally analyze the whole leg in standing posture. The optimal position that generates the most similar image is the captured X-ray image. The algorithm verifies the similarity using the performance of the proposed method by simulation-based experiments. Then, we analyzed the internal-external rotation angle of the femur using real patient data. Approximately 10.55 degrees of internal rotations were found relative to the defined anterior-posterior direction. In this paper, we present a useful registration method using the conventional X-ray image and 3D CT scan data to analyze the whole leg in the weight-bearing condition. },
}

@article{RN869,
  year = { 2018 },
  volume = { 5 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057951115{\&}doi=10.1523{\%}2FENEURO.0369-18.2018{\&}partnerID=40{\&}md5=904cdf49f286dcf83962f118f9096771 },
  type = { Journal Article },
  title = { Exogenous neural precursor cell transplantation results in structural and functional recovery in a hypoxic-ischemic hemiplegic mouse model },
  number = { 5 },
  keywords = { Cerebral palsy,Hypoxic-ischemia,Myelination,Neural precursor cells,Oligodendrocytes,White matter injury },
  journal = { eNeuro },
  issn = { 23732822 },
  doi = { 10.1523/ENEURO.0369-18.2018 },
  author = { Rumajogee and Altamentova and Li and Li and Wang and Kuurstra and Khazaei and Beldick and Menon and Kooy} and Fehlings },
  abstract = { Cerebral palsy (CP) is a common pediatric neurodevelopmental disorder, frequently resulting in motor and developmental deficits and often accompanied by cognitive impairments. A regular pathobiological hallmark of CP is oligodendrocyte maturation impairment resulting in white matter (WM) injury and reduced axonal myelination. Regeneration therapies based on cell replacement are currently limited, but neural precursor cells (NPCs), as cellular support for myelination, represent a promising regeneration strategy to treat CP, although the transplantation parameters (e.g., timing, dosage, mechanism) remain to be determined. We optimized a hemiplegic mouse model of neonatal hypoxia-ischemia that mirrors the pathobiological hallmarks of CP and transplanted NPCs into the corpus callosum (CC), a major white matter structure impacted in CP patients. The NPCs survived, engrafted, and differentiated morphologically in male and female mice. Histology and MRI showed repair of lesioned structures. Furthermore, electrophysiology revealed functional myelination of the CC (e.g., restoration of conduction velocity), while cylinder and CatWalk tests demonstrated motor recovery of the affected forelimb. Endogenous oligodendrocytes, recruited in the CC following transplantation of exogenous NPCs, are the principal actors in this recovery process. The lack of differentiation of the transplanted NPCs is consistent with enhanced recovery due to an indirect mechanism, such as a trophic and/or “bio-bridge�? support mediated by endogenous oligodendrocytes. Our work establishes that transplantation of NPCs represents a viable therapeutic strategy for CP treatment, and that the enhanced recovery is mediated by endogenous oligodendrocytes. This will further our understanding and contribute to the improvement of cellular therapeutic strategies. },
}

@article{RN884,
  year = { 2018 },
  volume = { 15 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057328183{\&}doi=10.1166{\%}2Fjctn.2018.7321{\&}partnerID=40{\&}md5=332bb09efa18d59dcf58b44028959bc7 },
  type = { Journal Article },
  title = { Mixture of dynamic textures applied to temporal analysis of lung lesions },
  pages = { 1839--1852 },
  number = { 6-7 },
  keywords = { Dynamic texture mixture,Lung cancer,Medical image,Temporal analysis,Tissue change detection },
  journal = { Journal of Computational and Theoretical Nanoscience },
  issn = { 15461963 },
  doi = { 10.1166/jctn.2018.7321 },
  author = { Quintanilha and Silva and {De Paiva} and Gattass },
  abstract = { Lung cancer is still one of the most popular types of cancer worldwide. The analysis of consecutive timely spaced CT scans is a useful tool to analyze the lesion's malignant behavior during treatment or for the follow up of lesions undetermined as to its nature (malignant or benign). This paper aims to propose a method to obtain more detailed information about the lesion changes through complementary studies on the biological activity of pulmonary nodules. The methodology presented uses mixture of dynamic textures to cluster different tissues of lung lesions, in accordance with the density change over time, and to describe regional changes using similarity measures. The study was conducted on two chest computed tomography databases. The Public Lung Database (PLD), which has lesions that undergo evaluation for drug therapy and a private database of lung lesions of undetermined diagnosis. The lesions from the public database had areas with density variations in the range from 0.01{\%} to 110.53{\%}. Lesions from the private database showed regions with density variations from 0.11{\%} to 46.94{\%} range. The density change analysis proposed, that is done by regionally different locations, provides more detailed information about their change over time. Lesions considered volumetrically stable may contain locations that suffer noticeable changes, as well as lesions with large volumetric growth may not show a significant change in density. },
}

@article{RN960,
  year = { 2018 },
  volume = { 6 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024392095{\&}doi=10.1080{\%}2F21681163.2017.1343686{\&}partnerID=40{\&}md5=627a45812ac92f205dffd7dce1423f77 },
  type = { Journal Article },
  title = { Hybrid rendering of exploded views for medical image atlas visualization },
  pages = { 668--677 },
  number = { 6 },
  keywords = { Biomedical visualisation,brain atlas,volume rendering },
  journal = { Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization },
  issn = { 21681171 },
  doi = { 10.1080/21681163.2017.1343686 },
  author = { McGraw and Guayaquil-Sosa },
  abstract = { Medical image atlases contain much information about human anatomy, but learning the shapes of anatomical regions and making sense of the overall structure defined in the atlas can be problematic. Atlases may contain hundreds of regions with complex shapes which can be tightly packed together. This makes visualisation difficult since the shapes can fit together in complex ways and visually obscure each other. In this work, we describe a technique which enables interactive exploration of medical image atlases that permits the hierarchical structure of the atlas and the content of an underlying medical image to be investigated simultaneously. Our method enables a user to create visualizations of the atlas similar to the exploded views used in technical illustrations to show the structure of mechanical assemblies. These views are constrained by the geometry of the atlas and the hierarchical structure to reduce the complexity of user interaction. We also enable the user to explode the atlas meshes themselves. The atlas meshes are registered with a medical image which is displayed on the cut surfaces of the meshes using raycasting. Results from the AAL human brain atlas are presented and discussed. },
}

@article{RN866,
  year = { 2018 },
  volume = { 4 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059431965{\&}doi=10.3390{\%}2Fjimaging4100123{\&}partnerID=40{\&}md5=3ef917586e8b50f8e99994a08d52d2c9 },
  type = { Journal Article },
  title = { An overview of watershed algorithm implementations in open source libraries },
  number = { 10 },
  keywords = { Computational complexity,Flooding,Memory consumption,Open source software,Processing speed,Rain falling,Watershed segmentation },
  journal = { Journal of Imaging },
  issn = { 2313433X },
  doi = { 10.3390/jimaging4100123 },
  author = { Kornilov and Safonov },
  abstract = { Watershed is a widespread technique for image segmentation. Many researchers apply the method implemented in open source libraries without a deep understanding of its characteristics and limitations. In the review, we describe benchmarking outcomes of six open-source marker-controlled watershed implementations for the segmentation of 2D and 3D images. Even though the considered solutions are based on the same algorithm by flooding having O(n)computational complexity, these implementations have significantly different performance. In addition, building of watershed lines grows processing time. High memory consumption is one more bottleneck for dealing with huge volumetric images. Sometimes, the usage of more optimal software is capable of mitigating the issues with the long processing time and insufficient memory space. We assume parallel processing is capable of overcoming the current limitations. However, the development of concurrent approaches for the watershed segmentation remains a challenging problem. },
}

@article{RN877,
  year = { 2018 },
  volume = { 17 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045392503{\&}doi=10.1186{\%}2Fs12938-018-0471-y{\&}partnerID=40{\&}md5=eb7e35093af28b9fca81c4e33c818b84 },
  type = { Journal Article },
  title = { Simulation of phase contrast angiography for renal arterial models },
  number = { 1 },
  keywords = { Blood flow simulation,Kidney vasculature modeling,MRI simulation,Phase contrast angiography,Vessel segmentation },
  journal = { BioMedical Engineering Online },
  issn = { 1475925X },
  doi = { 10.1186/s12938-018-0471-y },
  author = { Klepaczko and Szczypi{\'{n}}ski and Strzelecki and Stefa{\'{n}}czyk },
  abstract = { Background: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue. Monitoring of the kidney function is an example of such an application. Results: We present a computer framework for simulation of the PCA protocol, both conventional and accelerated with echo-planar imaging (EPI) readout, and its application to the numerical models of kidney vasculatures. Eight patient-specific renal arterial trees were reconstructed following vessel segmentation in real computed tomography angiograms. In addition, a synthetic model was designed using a vascular tree growth simulation algorithm. The results embrace a series of synthetic PCA images of the renal arterial trees giving insight into the image formation and quantification of kidney hemodynamics. Conclusions: The designed simulation framework enables quantification of the PCA measurement error in relation to ground-truth flow velocity data. The mean velocity measurement error for the reconstructed renal arterial trees range from 1.5 to 12.8{\%} of the aliasing velocity value, depending on image resolution and flip angle. No statistically significant difference was observed between measurements obtained using EPI with a number of echos (NETL) = 4 and conventional PCA. In case of higher NETL factors peak velocity values can be underestimated up to 34{\%}. },
}

@article{RN868,
  year = { 2018 },
  volume = { 99 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051412806{\&}doi=10.1016{\%}2Fj.gmod.2018.07.004{\&}partnerID=40{\&}md5=5d10dfa161a616c746fa9eae0597d362 },
  type = { Journal Article },
  title = { Accurate and reliable extraction of surfaces from image data using a multi-dimensional uncertainty model },
  pages = { 13--21 },
  keywords = { Parameter space exploration,Surface extraction,Uncertainty visualization },
  journal = { Graphical Models },
  issn = { 15240703 },
  doi = { 10.1016/j.gmod.2018.07.004 },
  author = { Gillmann and Wischgoll and Hamann and Hagen },
  abstract = { Surface extraction is an important step in the image processing pipeline to estimate the size and shape of an object. Unfortunately, state of the art surface extraction algorithms form a straight forward extraction based on a pre-defined value that can lead to surfaces, that are not accurate. Furthermore, most isosurface extraction algorithms lack the ability to communicate uncertainty originating from the image data. This can lead to a rejection of such algorithms in many applications. To solve this problem, we propose a methodology to extract and optimize surfaces from image data based on a defined uncertainty model. To identify optimal parameters, the presented method defines a parameter space that is evaluated and rates each extraction run based on the remaining surface uncertainty. The resulting surfaces can be explored intuitively in an interactive framework. We applied our methodology to a variety of datasets to demonstrate the quality of the resulting surfaces. },
}

@article{RN875,
  year = { 2018 },
  volume = { 174 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044150830{\&}doi=10.1016{\%}2Fj.neuroimage.2018.03.025{\&}partnerID=40{\&}md5=a707203bafd861c84c210833597b8ad5 },
  type = { Journal Article },
  title = { A comparison of publicly available linear MRI stereotaxic registration techniques },
  pages = { 191--200 },
  keywords = { Linear registration,MRI,Quality control },
  journal = { NeuroImage },
  issn = { 10959572 },
  doi = { 10.1016/j.neuroimage.2018.03.025 },
  author = { Dadar and Fonov and Collins },
  abstract = { Introduction: Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis. Methods: A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm{\_}affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method. Results: The average registration failure rate among all datasets was 27.41{\%}, 27.14{\%}, 12.74{\%}, 13.03{\%}, 0.44{\%} for the five versions of MRITOTAL techniques, 8.87{\%} for ANTs, 11.11{\%} for FSL, 12.35{\%} for Elastix Affine, 24.40{\%} for Elastix Similarity, and 30.66{\%} for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and atrophy related changes between passed and failed registrations. Conclusion: Our experiments show that the Revised BestLinReg had the best performance among the evaluated registration techniques while all techniques performed worse for images with higher levels of noise and non-uniformity as well as atrophy related changes. },
}

@article{RN865,
  year = { 2018 },
  volume = { 102 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041512823{\&}doi=10.1016{\%}2Fj.ijrobp.2017.12.280{\&}partnerID=40{\&}md5=acf090aa80c436bcbeed0e2695f55e04 },
  type = { Journal Article },
  title = { Short Diffusion Time Diffusion-Weighted Imaging With Oscillating Gradient Preparation as an Early Magnetic Resonance Imaging Biomarker for Radiation Therapy Response Monitoring in Glioblastoma: A Preclinical Feasibility Study },
  pages = { 1014--1023 },
  number = { 4 },
  journal = { International Journal of Radiation Oncology Biology Physics },
  issn = { 1879355X },
  doi = { 10.1016/j.ijrobp.2017.12.280 },
  author = { Bongers and Hau and Shen },
  abstract = { Purpose: To investigate a novel alternative diffusion-weighted imaging (DWI) approach using oscillating gradients preparation (OGSE) to obtain much shorter effective diffusion times ($\Delta$eff) for tumor response monitoring by apparent diffusion coefficient (ADC) mapping in a glioblastoma mouse model. Methods and Materials: Twenty-four BALB/c nude mice inoculated with U87 glioblastoma cells were randomized into a control group and an irradiation group, which underwent a 15-day fractioned radiation therapy (RT) course with 2 Gy/d. Therapy response was assessed by mapping of ADCs at 6 time points using an in-house implementation of a cos-OGSE DWI sequence with $\Delta$eff = 1.25 ms and compared with a standard pulsed gradient DWI protocol (PGSE) with typical clinical diffusion time $\Delta$eff = 18 ms. Longitudinal ADC changes in tumor and contralateral white matter (WM) were statistically assessed using repeated-measures analysis of variance and post hoc (Sidak) testing. Results: On short $\Delta$eff OGSE maps tumor ADC was generally 30{\%}-50{\%} higher than in surrounding WM. Areas correlated well with histology. Tumor identification was generally more difficult on PGSE maps owing to nonsignificant WM/tumor contrast. During RT, OGSE maps also showed significant tumor ADC increase (approximately 15{\%}) in response to radiation, consistently seen after 14-Gy RT dose. The clinical reference (PGSE) showed lower sensitivity to radiation changes, and no significant response across the radiation group and time course could be detected. Conclusion: Our short $\Delta$eff DWI method using OGSE better reflected histologically defined tumor areas and enabled more consistent and earlier detection of microstructural radiation changes than conventional methods. Oscillating gradients preparation offers significant potential as a robust microstructural RT response biomarker, potentially helping to shift important therapy decisions to earlier stages in the RT time course. },
}

@article{RN886,
  year = { 2018 },
  volume = { 91 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045980566{\&}doi=10.1259{\%}2Fbjr.20170663{\&}partnerID=40{\&}md5=fb09baba9be82130126233860365e1f2 },
  type = { Journal Article },
  title = { Accuracy and precision of manual segmentation of the maxillary sinus in MR images - A method study },
  number = { 1085 },
  journal = { British Journal of Radiology },
  issn = { 00071285 },
  doi = { 10.1259/bjr.20170663 },
  author = { Andersen and Darvann and Murakami and Larsen and Senda and Bilde and Buchwald and Kreiborg },
  abstract = { Objective: To assess the accuracy and precision of segmentation of the maxillary sinus in MR images to evaluate the potential usefulness of this modality in longitudinal studies of sinus development. Methods: A total of 15 healthy subjects who had been both craniofacial CT and MR scanned were included and the 30 maxillary sinus volumes were evaluated using segmentation. Two of the authors did segmentation of MRI and one of these authors did double segmentation. Agreement in results between CT and MRI as well as inter- and intraexaminer errors were evaluated by statistical and three-dimensional analysis. Results: The intraclass correlation coefficient for volume measurements for both method error, inter- and intraexaminer agreement were {\textgreater} 0.9 [maximal 95{\%} confidence interval of 0.989-0.997, p {\textless} 0.001] and the limit of agreement for all parameters were {\textless} 5.1{\%}. Segmentation errors were quantified in terms of overlap [Dice Coefficient (DICE) {\textgreater} 0.9 = excellent agreement] and border distance [95{\%} percentile Hausdorff Distance (HD) {\textless} 2 mm = acceptable agreement]. The results were replicable and not influenced by systematic errors. Conclusion: We found a high accuracy and precision of manual segmentation of the maxillary sinus in MR images. The largest mean errors were found close to the orbit and the teeth. Advances in knowledge: MRI can be used for 3D models of the paranasal sinuses with equally good results as CT and allows longitudinal follow-up of sinus development. },
}

@incollection{Kuegler2018,
  year = { 2018 },
  volume = { 10883 LNCS },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048674870{\&}doi=10.1007{\%}2F978-3-319-92258-4{\_}10{\&}partnerID=40{\&}md5=e5ef96e3629df844b717127296fb9d5c },
  type = { Serial },
  title = { Instrument pose estimation using registration for otobasis surgery },
  pages = { 105--114 },
  issn = { 16113349 },
  isbn = { 9783319922577 },
  doi = { 10.1007/978-3-319-92258-4_10 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { K{\"{u}}gler and Jastrzebski and Mukhopadhyay },
  abstract = { Clinical outcome of several Minimally Invasive Surgeries (MIS) heavily depend on the accuracy of intraoperative pose estimation of the surgical instrument from intraoperative x-rays. The estimation consists of finding the tool in a given set of x-rays and extracting the necessary data to recreate the tool's pose for further navigation - resulting in severe consequences of incorrect estimation. Though state-of-the-art MIS literature has exploited image registration as a tool for instrument pose estimation, lack of practical considerations in previous study design render their conclusion ineffective from a clinical standpoint. One major issue of such a study is the lack of Ground Truth in clinical data -as there are no direct ways of measuring the ground truth pose and indirect estimation accumulates error. A systematic way to overcome this problem is to generate Digitally Reconstructed Radiographs (DRR), however, such procedure generates data which are free from measuring errors (e.g. noise, number of projections), resulting claims of registration performance inconclusive. Generalization of registration performance across different instruments with different Degrees of Freedom (DoF) has not been studied as well. By marrying a rigorous study design involving several clinical scenarios with, for example, several optimizers, metrics and others parameters for image registration, this paper bridges this gap effectively. Although the pose estimation error scales inversely with instrument size, we show image registration generalizes well for different instruments and DoF. In particular, it is shown that increasing the number of x-ray projections can reduce the pose estimation error significantly across instruments - which might lead to the acquisition of several x-rays for pose estimation in a clinical workflow. },
}

@incollection{RN880,
  year = { 2018 },
  volume = { 27 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032361988{\&}doi=10.1007{\%}2F978-3-319-68195-5{\_}38{\&}partnerID=40{\&}md5=f66e1b53e39dbae26c9578f6ea426d78 },
  type = { Serial },
  title = { The rigid registration of CT and scanner dataset for computer aided surgery },
  publisher = { Springer Netherlands },
  pages = { 345--353 },
  issn = { 22129413 },
  isbn = { 22129391 (ISSN) },
  doi = { 10.1007/978-3-319-68195-5_38 },
  booktitle = { Lecture Notes in Computational Vision and Biomechanics },
  author = { \'{S}}wi{\c{a}}tek-Najwer and {\.{Z}}uk and Majak and Popek },
  abstract = { The main aim of this work was to perform rigid registration of Computed Tomography (CT) and scanner datasets. The surgeon applies CT and scanner datasets in computer aided surgery and performs registration in order to visualize the location of surgical instrument on screen. It is well known fact that the registration procedure is crucial for efficient computer aiding of surgery. Selected algorithm should take into account types of datasets, required accuracy and time of calculations. The algorithms are classified basing on the various criteria: e.g. precision (coarse and fine registration), types of pointset (set of pair of corresponding points – so called point-point method, unorganized sets of points – so called surface registration). The paper presents exemplary results of applying the following algorithms: Landmark Transform (point-point registration), two methods of uninitialized Iterative Closest Point type (surface registration) and a hybrid method. The evaluated factors were: distance error (mean, minimal and maximal value) and running time of algorithm. The algorithms were tested on various datasets: (1) two similar datasets from Computed Tomography (one is geometrically transformed), (2) Computed Tomography dataset and cloud of points recorded using 3D Artec Space Spider scanner. In the first case the mean error values equaled: 102.08 mm – 121.70 mm for uninitialized ICPs methods, 0.005 mm for Landmark Transform method, and 0.0003 mm for hybrid method. The slowest algorithms in our tests were ICPs methods, faster was hybrid algorithm, and the fastest was Landmark Transform method. In the second case the distance errors were evaluated in four selected points, and the smallest errors were: 23.21 mm for uninitialized ICPs method, 0.69 mm for Landmark Transform, 9.03 for hybrid method. All algorithms were relatively slow for these large datasets, the fastest was Landmark Transform. In the second part of research we analysed the Target Registration Error (TRE) for fused Computed Tomography and scanner-recorded dataset. The TRE values equaled 0.7 mm - 2.8 mm. The results of CT – scanner datasets registration highly depend on the similarity of sets, especially their overlapping, but also their resolutions and uniformities. },
}

@incollection{RN879,
  year = { 2018 },
  volume = { 681 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031410415{\&}doi=10.1007{\%}2F978-3-319-68720-9{\_}2{\&}partnerID=40{\&}md5=333a82c9184f554ff151234b1617f13b },
  type = { Serial },
  title = { CT–SPECT analyzer - A tool for CT and SPECT data fusion and volumetric visualization },
  publisher = { Springer Verlag },
  pages = { 11--18 },
  keywords = { CT,Data fusion,SPECT,Volumetric visualization },
  issn = { 21945357 },
  isbn = { 9783319687193 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-3-319-68720-9_2 },
  booktitle = { Advances in Intelligent Systems and Computing },
  author = { Listewnik and Piwowarska-Bilska and Safranow and Iwanowski and Laszczy{\'{n}}ska and Chosia and Ostrowski and Birkenfeld and Mazurek },
  abstract = { Data fusion and specific visualization of CT and SPECT are important for diagnosis and research purposes. Selected problems are considered in the paper and are related to the developed CT–SPECT Analyzer software. Hierarchical mapping with SPECT priority for maximum value of rays is applied in this software. Three variants of color mappings are presented. Some practical aspects related to low quality of CT are considered also. The most promising is the rainbow gradient with gamma curve adjustment. },
}

@incollection{RN975,
  year = { 2018 },
  volume = { 836 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053181661{\&}doi=10.1007{\%}2F978-981-13-1343-1{\_}53{\&}partnerID=40{\&}md5=65a97c1936282996fbe6ce8dfeb09631 },
  type = { Serial },
  title = { Optimal Geometric Active Contours: Application to Human Brain Segmentation },
  publisher = { Springer Verlag },
  pages = { 646--657 },
  keywords = { Dice similarity coefficient,Geometric active contour,Jaccard similarity coefficient,Level set,Magnetic resonance imaging (MRI) },
  issn = { 18650929 },
  isbn = { 9789811313424 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-981-13-1343-1_53 },
  booktitle = { Communications in Computer and Information Science },
  author = { Biswas and Maity and Bhattacharya },
  abstract = { An efficient Segmentation of lateral ventricles plays a vital role in quantitatively analyzing the global and regional information in magnetic resonance imaging (MRI) of human brain. In this paper, a semi automatic segmentation methodology to support the study of efficient pathologies of the lateral ventricles along with white matter and gray matter of human brain is proposed. The segmentation is executed using an optimal geometric active contour with level set methods. A nominal anatomical knowledge is incorporated into the methodology in order to choose the most probable surfaces of the lateral ventricles of human brain, even if they are disconnected, and to eliminate addition of non ventricle cerebrospinal fluid (CSF) regions. The proposed segmentation method is applied to multislice MRI data and compared with region growing algorithms. The results Dice similarity coefficient 0.955, Jaccard similarity coefficient 0.815 demonstrates the reliability and efficiency. },
}

@inproceedings{Fishbaugh2018a,
  year = { 2018 },
  volume = { 2018-April },
  url = { https://ieeexplore.ieee.org/document/8363742/ {\%}3CGo to },
  type = { Book Section },
  title = { Estimating shape correspondence for populations of objects with complex topology },
  series = { IEEE International Symposium on Biomedical Imaging },
  publisher = { Ieee },
  pages = { 1010--1013 },
  month = { apr },
  keywords = { Complex topology,Diffeomorphic shape registration,Orthognathic surgery,Statistical shape analysis,Statistical shape modeling },
  issn = { 19458452 },
  isbn = { 9781538636367 },
  doi = { 10.1109/ISBI.2018.8363742 },
  booktitle = { Proceedings - International Symposium on Biomedical Imaging },
  author = { Fishbaugh and Pascal and Fischer and Nguyen and Boen and Goncalves and Gerig and Paniagua },
  address = { New York },
  abstract = { Statistical shape analysis captures the geometric properties of a given set of shapes, obtained from medical images, by means of statistical methods. Orthognathic surgery is a type of craniofacial surgery that is aimed at correcting severe skeletal deformities in the mandible and maxilla. Methods assuming spherical topology cannot represent the class of anatomical structures exhibiting complex geometries and topologies, including the mandible. In this paper we propose methodology based on non-rigid deformations of 3D geometries to be applied to objects with thin, complex structures. We are able to accurately and quantitatively characterize bone healing at the osteotomy site as well as condylar remodeling for three orthognathic surgery cases, demonstrating the effectiveness of the proposed methodology. },
}

@inproceedings{Medina2017,
  year = { 2018 },
  volume = { 2017-Janua },
  url = { {\%}3CGo to https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045738426{\&}doi=10.1109{\%}2FETCM.2017.8247499{\&}partnerID=40{\&}md5=31a12ff7f15e6264e843b9c8cc503a7c },
  type = { Book },
  title = { Accuracy of connected confidence left ventricle segmentation in 3-D multi-slice computerized tomography images },
  series = { 2017 Ieee Second Ecuador Technical Chapters Meeting },
  publisher = { Ieee },
  pages = { 1--6 },
  keywords = { Connected confidence,Left ventricle segmentation,Multi-slice computerized tomography,Software platform },
  isbn = { 9781538638941 },
  doi = { 10.1109/ETCM.2017.8247499 },
  booktitle = { 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017 },
  author = { Medina and Bautista and Morocho },
  address = { New York },
  abstract = { Cardiovascular diseases are the main cause of death in the World. This fact has motivated different actions for prevention, diagnosis and monitoring of cardiovascular diseases. In this work, the accuracy of a connected confidence left ventricle segmentation method is performed. This task is accomplished using a software platform for left ventricle segmentation of 3-D cardiac Multi-Slice Computerized Tomography (MSCT) images that is also described. The software platform has as a goal performing research about efficient methods for cardiac image segmentation and quantification. The accuracy assessment of the segmentation method is performed by comparing the estimated segmentation with respect to segmentations manually traced by cardiologists. Results show that the segmentation method provides Dice Similarity coefficients higher than 0.90 with low computational cost. The obtained segmentation is able to include within the left ventricular lumen the papillary trabeculae muscles, enabling further accurate estimation of the left ventricular mass. },
}

@inproceedings{Paniagua2018,
  year = { 2018 },
  volume = { 10578 },
  url = { https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10578/2293603/Automatic-quantification-framework-to-detect-cracks-in-teeth/10.1117/12.2293603.full },
  title = { Automatic quantification framework to detect cracks in teeth },
  publisher = { SPIE },
  pages = { 55 },
  month = { mar },
  issn = { 0277-786X },
  isbn = { 9781510616455 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2293603 },
  booktitle = { Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging },
  author = { Paniagua and Shah and Hernandez-Cerdan and Budin and Chittajallu and Walter and Mol and Khan and Vimort },
  abstract = { Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth. },
}

@inproceedings{RN864,
  year = { 2018 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068362296{\&}doi=10.1109{\%}2FITOEC.2018.8740656{\&}partnerID=40{\&}md5=1c3ff161aa23226e508668105edd9d2f },
  type = { Conference Proceedings },
  title = { SPIFFY: A simpler image viewer for medical imaging },
  publisher = { Institute of Electrical and Electronics Engineers Inc. },
  pages = { 297--301 },
  keywords = { Cognitive walkthrough evaluation,Human-computer interaction,Medical imaging,Visualizing software },
  isbn = { 9781538653739 },
  editor = { [object Object] },
  doi = { 10.1109/ITOEC.2018.8740656 },
  booktitle = { Proceedings of 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference, ITOEC 2018 },
  author = { Sun and Chandra },
  abstract = { Medical imaging visualization technology takes a significant role in the medical community. With the assistance of medical imaging visualization applications, huge convenience has been brought into clinical diagnosis, monitoring, and treatment. It allows doctors and researchers to see inside the human body, to identify medical problems, and to diagnose diseases. This article presents a lightweight, fast and user-friendly image viewer for medical imaging called SPIFFY. Some developing methodologies with the integration of VTK, ITK, and Qt will be presented in this article. Besides, the minimalist user interface(UI) design of SPIFFY with an application of Human-Computer Interaction(HCI) psychology principles will also be introduced. Moreover, this article will identify the benefits provided by SPIFFY and present a benchmark against some existing medical visualization applications. Experiments using cognitive walkthrough evaluation shows that SPIFFY provides both high effectiveness and efficiency. },
}

@inproceedings{RN883,
  year = { 2018 },
  volume = { 10975 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060546679{\&}doi=10.1117{\%}2F12.2506687{\&}partnerID=40{\&}md5=5125dd9c739585e2fc9a96de92ac84ae },
  type = { Conference Proceedings },
  title = { Intrapatient multimodal medical image registration of brain CT-MRI 3D: an approach based on metaheuristics },
  publisher = { SPIE },
  pages = { 8 },
  issn = { 1996756X },
  isbn = { 9781510626058 },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.1117/12.2506687 },
  booktitle = { 14th International Symposium on Medical Information Processing and Analysis, SIPAIM 2018 },
  author = { {Cespedes Sanchez} and {Legal Ayala} and {Pinto Roa} and Gimenez and Lopez and {Vazquez Noguera} },
}

@inproceedings{RN974,
  year = { 2018 },
  volume = { 133 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051328846{\&}doi=10.1016{\%}2Fj.procs.2018.07.084{\&}partnerID=40{\&}md5=e21c6be1fb5f5e12be609a506f661f8b },
  type = { Conference Proceedings },
  title = { An efficient volumetric segmentation of cerebral lateral ventricles },
  publisher = { Elsevier B.V. },
  pages = { 561--568 },
  keywords = { Geodesic active contour,Lateral Ventricle,MR Images,Volumetric,level set methods },
  issn = { 18770509 },
  isbn = { 18770509 (ISSN) },
  editor = { [object Object],[object Object] },
  doi = { 10.1016/j.procs.2018.07.084 },
  booktitle = { Procedia Computer Science },
  author = { Biswas and Bhattacharya and Maity },
  abstract = { Human brain is a set of four communicating network of ventricles heaving with cerebrospinal fluid (CSF) which is located inside the brain parenchyma. An efficient segmentation of cerebral lateral ventricles one in each hemisphere can support the study of efficient pathologies for successful conclusion of various diseases. In this paper, an efficient and fast energy optimised technique for volumetric segmentation of lateral ventricles from MR images of human brain is proposed which is based on geodesic active contours using level set method. The proposed approach consists of mainly four main stages: 1. Preprocessing stage, 2. Presegmentation stage, 3. Contour Evolution with Energy optimisation stage, 4. Termination stage. Experiments on multislice MRI data obtained dice coefficient of 0.955, jaccard coefficient of 0.915 and other surface distance measures demonstrate the advantages of the proposed approach in both accuracy and efficiency. },
}

@book{Ponzio2017,
  year = { 2017 },
  url = { {\%}3CGo to },
  type = { Book },
  title = { A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems Integrating MR and US for Minimally Invasive Neuroimaging },
  series = { Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol 2: Bioimaging },
  publisher = { Scitepress },
  pages = { 114--121 },
  isbn = { 978-989-758-215-8 },
  doi = { 10.5220/0006239201140121 },
  author = { Ponzio and Macii and Ficarra and {Di Cataldo} },
  address = { Setubal },
}

@inbook{Tran2017,
  year = { 2017 },
  volume = { 10067 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Mapping in-vivo optic nerve head strains caused by intraocular and intracranial pressures },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 100670B },
  issn = { 0277-786X },
  isbn = { 9781510605756 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2257360 },
  booktitle = { Optical Elastography and Tissue Biomechanics IV },
  author = { Tran and Grimm and Wang and Smith and Gogola and Nelson and Tyler-Kabara and Schuman and Wollstein and Sigal },
  address = { Bellingham },
  abstract = { {\textcopyright} 2017 SPIE. Although it is well documented that abnormal levels of either intraocular (IOP) or intracranial pressure (ICP) can lead to potentially blinding conditions, such as glaucoma and papilledema, little is known about how the pressures actually affect the eye. Even less is known about potential interplay between their effects, namely how the level of one pressure might alter the effects of the other. Our goal was to measure in-vivo the pressure-induced stretch and compression of the lamina cribrosa due to acute changes of IOP and ICP. The lamina cribrosa is a structure within the optic nerve head, in the back of the eye. It is important because it is in the lamina cribrosa that the pressure-induced deformations are believed to initiate damage to neural tissues leading to blindness. An eye of a rhesus macaque monkey was imaged in-vivo with optical coherence tomography while IOP and ICP were controlled through cannulas in the anterior chamber and lateral ventricle, respectively. The image volumes were analyzed with a newly developed digital image correlation technique. The effects of both pressures were highly localized, nonlinear and non-monotonic, with strong interactions. Pressure variations from the baseline normal levels caused substantial stretch and compression of the neural tissues in the posterior pole, sometimes exceeding 20{\%}. Chronic exposure to such high levels of biomechanical insult would likely lead to neural tissue damage and loss of vision. Our results demonstrate the power of digital image correlation technique based on non-invasive imaging technologies to help understand how pressures induce biomechanical insults and lead to vision problems. },
}

@inbook{Hoehme2017,
  year = { 2017 },
  volume = { 1506 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Creation of three-dimensional liver tissue models from experimental images for systems medicine },
  series = { Methods in Molecular Biology },
  publisher = { Humana Press Inc },
  pages = { 319--362 },
  keywords = { 2D/3D microscopy,Confocal scanning microscopy,Hepatocyte transplantation,Liver architecture,Liver tissue model,Spatiotemporal model,Systems biology,Systems medicine,TiQuant software },
  issn = { 10643745 },
  isbn = { 978-1-4939-6506-9; 978-1-4939-6504-5 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-1-4939-6506-9_22 },
  booktitle = { Methods in Molecular Biology },
  author = { Hoehme and Friebel and Hammad and Drasdo and Hengstler },
  address = { Totowa },
  abstract = { In this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine. },
}

@inbook{Gamarra2017,
  year = { 2017 },
  volume = { 569 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { A service-oriented architecture for bioinformatics: An application in cell image analysis },
  series = { Advances in Intelligent Systems and Computing },
  publisher = { Springer-Verlag Berlin },
  pages = { 724--734 },
  keywords = { Bioinformatic,Cloud computing,Image processing,SOA },
  issn = { 21945357 },
  isbn = { 9783319565347 },
  editor = { [object Object],[object Object],[object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-319-56535-4_71 },
  booktitle = { Advances in Intelligent Systems and Computing },
  author = { Gamarra and Zurek and Nieto and Jimeno and Sierra },
  address = { Berlin },
  abstract = { The advance technology in microscopy and computing has allowed the development of cell image analysis. Cloud Computing offers services, software and computing infrastructure to manage cell images' big data. However the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. In this paper we present a framework to develop a software solution with a Service-Oriented Architecture (SOA) applied to the analysis of cell images using cloud computing. },
}

@article{shaffer2017longitudinal,
  year = { 2017 },
  volume = { 38 },
  url = { http://www.ncbi.nlm.nih.gov/pubmed/28045213{\%}0Ahttp://doi.wiley.com/10.1002/hbm.23465 },
  title = { Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration },
  pmid = { 28045213 },
  pages = { 1460--1477 },
  number = { 3 },
  keywords = { Huntington disease,computer-assisted,diffusion magnetic resonance imaging,diffusion tractography,disease progression,image processing,multicenter study,prodromal,white matter },
  journal = { Human Brain Mapping },
  issn = { 10970193 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Shaffer et al/Human Brain Mapping/Shaffer et al. - 2017 - Longitudinal diffusion changes in prodromal and early HD Evidence of white-matter tract deterioration.pdf:pdf },
  doi = { 10.1002/hbm.23465 },
  author = { Shaffer and Ghayoor and Long and Kim and Lourens and O'Donnell and Westin and Rathi and Magnotta and Paulsen and Johnson },
  annote = { From Duplicate 2 (Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration - Shaffer, Joseph J.; Ghayoor, Ali; Long, Jeffrey D.; Kim, Regina EY Y; Lourens, Spencer; O'Donnell, Lauren J.; Westin, Carl-Fredrik; Rathi, Yogesh; Magnotta, Vincent; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration - Shaffer, Joseph J.; Ghayoor, Ali; Long, Jeffrey D.; Kim, Regina EY; Lourens, Spencer; O'Donnell, Lauren J.; Westin, Carl-Fredrik; Rathi, Yogesh; Magnotta, Vincent; Paulsen, Jane S.; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration - Shaffer, Joseph J; Ghayoor, Ali; Long, Jeffrey D; Kim, Regina E Y; Lourens, Spencer; O'Donnell, Lauren J; Westin, Carl-Fredrik; Rathi, Yogesh; Magnotta, Vincent; Paulsen, Jane S; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#} },
  abstract = { Introduction: Huntington's disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). Methods: As part of the PREDICT-HD study, N = 191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1–5 year follow-up) DWI, with n = 649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. Results: Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. Conclusions: This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the premotor area and the putamen, which may be closely tied to the onset of motor symptoms in HD. Hum Brain Mapp 38:1460–1477, 2017. {\textcopyright} 2017 Wiley Periodicals, Inc. },
}

@article{Wolfelschneider2017,
  year = { 2017 },
  volume = { 44 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates },
  pages = { 2066--2076 },
  number = { 6 },
  keywords = { 4D dose reconstruction,4DCT,Respiratory motion model,Scale invariant feature transform,Surrogates },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.12243 },
  author = { Wolfelschneider and Seregni and Fassi and Ziegler and Baroni and Fietkau and Riboldi and Bert },
  abstract = { Purpose: The aim of this study was to evaluate a surrogate-driven motion model based on fourdimensional computed tomography that is able to predict CT volumes corresponding to arbitrary respiratory phases. Furthermore, the comparison of three different driving surrogates is examined and the feasibility of using the model for 4D dose re-calculation will be discussed. Methods: The study is based on repeated 4DCTs of twenty patients treated for bronchial carcinoma and metastasis. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the model considers inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. The estimated volumes resulting from the model were compared to ground-truth clinical 4DCTs using absolute HU differences in the lung region and landmarks localized using the Scale Invariant Feature Transform. Finally, the c-index was used to evaluate the dosimetric effects of the intensity differences measured between the estimated and the ground-truth CTvolumes. Results: The results show absolute HU differences between estimated and ground-truth images with median value (± standard deviation) of (61.3 ± 16.7) HU. Median 3D distances, measured on about 400 matching landmarks in each volume, were (2.9 ± 3.0) mm. 3D errors up to 28.2 mm were found for CT images with artifacts or reduced quality. Pass rates for all surrogate approaches were above 98.9{\%} with a c-criterion of 2{\%}/2 mm. Conclusion: The results depend mainly on the image quality of the initial 4DCT and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however, uncertainties decrease for volumetric approaches. Application of the model for 4D dose calculations is feasible. },
}

@article{Tourbier2017,
  year = { 2017 },
  volume = { 155 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automated template-based brain localization and extraction for fetal brain MRI reconstruction },
  pages = { 460--472 },
  keywords = { B-Spline deformation,Block matching,Brain localization,Fetal brain MRI,Slice-by-slice brain extraction,Slice-to-template registration,Super-resolution reconstruction },
  journal = { NeuroImage },
  issn = { 10959572 },
  doi = { 10.1016/j.neuroimage.2017.04.004 },
  author = { Tourbier and Velasco-Annis and Taimouri and Hagmann and Meuli and Warfield and {Bach Cuadra} and Gholipour },
  abstract = { Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5{\%}. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks. },
}

@article{Schmitz2017,
  year = { 2017 },
  volume = { 7 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids },
  pages = { 13 },
  journal = { Scientific Reports },
  issn = { 20452322 },
  doi = { 10.1038/srep43693 },
  author = { Schmitz and Fischer and Mattheyer and Pampaloni and Stelzer },
  abstract = { Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid's size and varies between 50{\%} and 75{\%} of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 10 5 to 1 × 10 6 cells/mm 3. Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture. },
}

@article{Rak2017,
  year = { 2017 },
  volume = { 12 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Joint deformable liver registration and bias field correction for MR-guided HDR brachytherapy },
  pages = { 2169--2180 },
  number = { 12 },
  keywords = { Bias field correction,Deformable registration,High-dose rate brachytherapy,Liver intervention,Magnetic resonance imaging },
  journal = { International Journal of Computer Assisted Radiology and Surgery },
  issn = { 18616429 },
  doi = { 10.1007/s11548-017-1633-2 },
  author = { Rak and K{\"{o}}nig and T{\"{o}}nnies and Walke and Ricke and Wybranski },
  abstract = { Purpose: In interstitial high-dose rate brachytherapy, liver cancer is treated by internal radiation, requiring percutaneous placement of applicators within or close to the tumor. To maximize utility, the optimal applicator configuration is pre-planned on magnetic resonance images. The pre-planned configuration is then implemented via a magnetic resonance-guided intervention. Mapping the pre-planning information onto interventional data would reduce the radiologist's cognitive load during the intervention and could possibly minimize discrepancies between optimally pre-planned and actually placed applicators. Methods: We propose a fast and robust two-step registration framework suitable for interventional settings: first, we utilize a multi-resolution rigid registration to correct for differences in patient positioning (rotation and translation). Second, we employ a novel iterative approach alternating between bias field correction and Markov random field deformable registration in a multi-resolution framework to compensate for non-rigid movements of the liver, the tumors and the organs at risk. In contrast to existing pre-correction methods, our multi-resolution scheme can recover bias field artifacts of different extents at marginal computational costs. Results: We compared our approach to deformable registration via B-splines, demons and the SyN method on 22 registration tasks from eleven patients. Results showed that our approach is more accurate than the contenders for liver as well as for tumor tissues. We yield average liver volume overlaps of 94.0 ± 2.7{\%} and average surface-to-surface distances of 2.02 ± 0.87 mm and 3.55 ± 2.19 mm for liver and tumor tissue, respectively. The reported distances are close to (or even below) the slice spacing (2.5 – 3.0 mm) of our data. Our approach is also the fastest, taking 35.8 ± 12.8 s per task. Conclusion: The presented approach is sufficiently accurate to map information available from brachytherapy pre-planning onto interventional data. It is also reasonably fast, providing a starting point for computer-aidance during intervention. },
}

@article{Price2017,
  year = { 2017 },
  volume = { 18 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Optimization of a novel large field of view distortion phantom for MR-only treatment planning },
  pages = { 51--61 },
  number = { 4 },
  keywords = { Distortion,Gradient nonlinearity,MRI,Phantom,Spatial accuracy },
  journal = { Journal of Applied Clinical Medical Physics },
  issn = { 15269914 },
  doi = { 10.1002/acm2.12090 },
  author = { Price and Knight and Hwang and Bayram and Nejad-Davarani and Glide-Hurst },
  abstract = { Purpose: MR-only treatment planning requires images of high geometric fidelity, particularly for large fields of view (FOV). However, the availability of large FOV distortion phantoms with analysis software is currently limited. This work sought to optimize a modular distortion phantom to accommodate multiple bore configurations and implement distortion characterization in a widely implementable solution. Method and Materials: To determine candidate materials, 1.0 T MR and CT images were acquired of twelve urethane foam samples of various densities and strengths. Samples were precision-machined to accommodate 6 mm diameter paintballs used as landmarks. Final material candidates were selected by balancing strength, machinability, weight, and cost. Bore sizes and minimum aperture width resulting from couch position were tabulated from the literature (14 systems, 5 vendors). Bore geometry and couch position were simulated using MATLAB to generate machine-specific models to optimize the phantom build. Previously developed software for distortion characterization was modified for several magnet geometries (1.0 T, 1.5 T, 3.0 T), compared against previously published 1.0 T results, and integrated into the 3D Slicer application platform. Results: All foam samples provided sufficient MR image contrast with paintball landmarks. Urethane foam (compressive strength {\~{}}1000 psi, density {\~{}}20 lb/ft3) was selected for its accurate machinability and weight characteristics. For smaller bores, a phantomversion with the following parameters was used: 15 foam plates, 55 9 55 9 37.5 cm3 (L9W9H), 5,082 landmarks, and weight {\~{}}30 kg. To accommodate {\textgreater} 70 cm wide bores, an extended build used 20 plates spanning 55 9 55 9 50 cm3 with 7,497 landmarks and weight {\~{}}44 kg.Distortion characterization softwarewas implemented as an externalmodule into 3DSlicer's plugin framework and results agreed with the literature. Conclusion: The design and implementation of a modular, extendable distortion phantom was optimized for several bore configurations. The phantom and analysis software will be available for multi-institutional collaborations and cross-validation trials to support MR-only planning. },
}

@article{Park2017,
  year = { 2017 },
  volume = { 12 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A magnetic resonance image-guided breast needle intervention robot system: overview and design considerations },
  pages = { 1319--1331 },
  number = { 8 },
  keywords = { Bendable needle,Breast cancer,Magnetic resonance imaging,Medical robotics,Needle intervention },
  journal = { International Journal of Computer Assisted Radiology and Surgery },
  issn = { 18616429 },
  doi = { 10.1007/s11548-017-1528-2 },
  author = { Park and Kim and Lim and Yoon and Kim and Kang and Jo },
  abstract = { Purpose: We developed an image-guided intervention robot system that can be operated in a magnetic resonance (MR) imaging gantry. The system incorporates a bendable needle intervention robot for breast cancer patients that overcomes the space limitations of the MR gantry. Methods: Most breast coil designs for breast MR imaging have side openings to allow manual localization. However, for many intervention procedures, the patient must be removed from the gantry. A robotic manipulation system with integrated image guidance software was developed. Our robotic manipulator was designed to be slim, so as to fit between the patient's side and the MR gantry wall. Only non-magnetic materials were used, and an electromagnetic shield was employed for cables and circuits. The image guidance software was built using open source libraries. In situ feasibility tests were performed in a 3-T MR system. One target point in the breast phantom was chosen by the clinician for each experiment, and our robot moved the needle close to the target point. Results: Without image-guided feedback control, the needle end could not hit the target point (distance = 5 mm) in the first experiment. Using our robotic system, the needle hits the target lesion of the breast phantom at a distance of 2.3 mm from the same target point using image-guided feedback. The second experiment was performed using other target points, and the distance between the final needle end point and the target point was 0.8 mm. Conclusions: We successfully developed an MR-guided needle intervention robot for breast cancer patients. Further research will allow the expansion of these interventions. },
}

@article{Narizzano2017,
  year = { 2017 },
  volume = { 18 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { SEEG assistant: A 3DSlicer extension to support epilepsy surgery },
  pages = { 13 },
  number = { 1 },
  keywords = { Automatic segmentation,Epilepsy,Epileptic zone detections,GMPI,Medical imaging,SEEG },
  journal = { BMC Bioinformatics },
  issn = { 14712105 },
  doi = { 10.1186/s12859-017-1545-8 },
  author = { Narizzano and Arnulfo and Ricci and Toselli and Tisdall and Canessa and Fato and Cardinale },
  abstract = { Background: In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist's workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors. Results: In this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing. Conclusions: The SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools. },
}

@article{Meyer2017,
  year = { 2017 },
  volume = { 298 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Beyond the stony veil: Reconstructing the Earth's earliest large animal traces via computed tomography X-ray imaging },
  pages = { 341--350 },
  keywords = { Bioturbation,Ediacaran,Lamonte trevallis,South China,Trace fossil },
  journal = { Precambrian Research },
  issn = { 03019268 },
  doi = { 10.1016/j.precamres.2017.05.010 },
  author = { Meyer and Polys and Yaqoob and Hinnov and Xiao },
  abstract = { Trace fossils are superb lines of evidence for examining the ancient biologic world because they offer an opportunity to infer behavioral ecology of organisms. However, traces can be difficult to parse from their matrix, which leads to the loss of important morphological and behavioral data. This is especially true for the earliest marine animal traces from the Ediacaran Period (635–541 Ma), which are usually small ({\textless}5 mm in diameter) and simple (mostly small horizontal trails and burrows), and are sometimes difficult to be distinguished from co-existing tubular body fossils. There is also evidence that the prevalence of microbial substrates in Ediacaran oceans may have influenced emerging trace makers in non-actualistic ways from a late Phanerozoic perspective (e.g., microbial mats may have facilitated a strong geochemical gradient across the sediment-water interface). Therefore, the discovery of the relatively large traces of Lamonte trevallis from the Ediacaran Shibantan Member of the Denying Formation (∼551–541 Ma) in the Yangtze Gorges area of South China provides a unique opportunity to study early bioturbators. These trace fossils are large enough and have sufficient compositional contrast (relative to the matrix) for in situ analysis via X-ray computed tomography (CT) and microcomputed tomography (microCT). Each analytical method has its own advantages and disadvantages. CT scans can image larger specimens, but cannot adequately resolve small features of interest. MicroCT scans can achieve higher resolution, but can only be used with small samples and may involve more post-processing than CT scans. As demonstrated in this study, X-ray CT and microCT in combination with other 3D imaging techniques and resources have the potential to resolve the 3D morphology of Ediacaran trace fossils. A new Volumetric Bioturbation Intensity (VBI) is also proposed, which quantifies whole rock bioturbation using 3D analysis of subsurface traces. Combined with the ability to examine trace fossils in situ, the VBI can enhance our view of ancient ecologies and life's enduring relationship with sediments. },
}

@article{Maga2017,
  year = { 2017 },
  volume = { 231 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis },
  pages = { 433--443 },
  number = { 3 },
  keywords = { geometrics morphometrics,image processing,image-based shape analysis,landmarking,microCT,segmentation },
  journal = { Journal of Anatomy },
  issn = { 14697580 },
  doi = { 10.1111/joa.12645 },
  author = { Maga and Tustison and Avants },
  abstract = { Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene–environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4–12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template. },
}

@article{Liu2017,
  year = { 2017 },
  volume = { 8 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { In vivo pentamodal tomographic imaging for small animals },
  pages = { 1356 },
  number = { 3 },
  journal = { Biomedical Optics Express },
  issn = { 2156-7085 },
  doi = { 10.1364/boe.8.001356 },
  author = { Liu and Guo and Liu and Zhang and Chi and Hui and Dong and Hu and Tian },
  abstract = { {\textcopyright} 2017 Optical Society of America. Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine. },
}

@article{Lee2017,
  year = { 2017 },
  volume = { 277 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { COMETS2: An advanced MATLAB toolbox for the numerical analysis of electric fields generated by transcranial direct current stimulation },
  pages = { 56--62 },
  keywords = { Electrostatic field,Finite element method (FEM),MATLAB toolbox,Neuromodulation,Transcranial direct current stimulation (tDCS) },
  journal = { Journal of Neuroscience Methods },
  issn = { 1872678X },
  doi = { 10.1016/j.jneumeth.2016.12.008 },
  author = { Lee and Jung and Lee and Im },
  abstract = { Background Since there is no way to measure electric current generated by transcranial direct current stimulation (tDCS) inside the human head through in vivo experiments, numerical analysis based on the finite element method has been widely used to estimate the electric field inside the head. In 2013, we released a MATLAB toolbox named COMETS, which has been used by a number of groups and has helped researchers to gain insight into the electric field distribution during stimulation. The aim of this study was to develop an advanced MATLAB toolbox, named COMETS2, for the numerical analysis of the electric field generated by tDCS. New method COMETS2 can generate any sizes of rectangular pad electrodes on any positions on the scalp surface. To reduce the large computational burden when repeatedly testing multiple electrode locations and sizes, a new technique to decompose the global stiffness matrix was proposed. Results As examples of potential applications, we observed the effects of sizes and displacements of electrodes on the results of electric field analysis. The proposed mesh decomposition method significantly enhanced the overall computational efficiency. Comparison with existing methods We implemented an automatic electrode modeler for the first time, and proposed a new technique to enhance the computational efficiency. Conclusions In this paper, an efficient toolbox for tDCS analysis is introduced (freely available at http://www.cometstool.com). It is expected that COMETS2 will be a useful toolbox for researchers who want to benefit from the numerical analysis of electric fields generated by tDCS. },
}

@article{Kiss2017,
  year = { 2017 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Segmentation of 3D images of plant tissues at multiple scales using the level set method },
  pages = { 11 },
  number = { 1 },
  keywords = { 3D,Cell,Cellwall,Confocal image,L1,Level set method,Nucleus,Segmentation,Watershed },
  journal = { Plant Methods },
  issn = { 17464811 },
  doi = { 10.1186/s13007-017-0264-5 },
  author = { Kiss and Moreau and Mirabet and Calugaru and Boudaoud and Das },
  abstract = { Background: Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue. Results: We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue. Conclusion: The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations. },
}

@article{Jakubicek2018,
  year = { 2017 },
  volume = { 14 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Vertebrae Segmentation in 3D CT Data: A Review of Methods and Evaluation Approaches },
  pages = { 853--866 },
  number = { 6 },
  journal = { Current Medical Imaging Reviews },
  issn = { 15734056 },
  doi = { 10.2174/1573405613666170622120228 },
  author = { Jakubicek and Chmelik and Jan },
  abstract = { {\textcopyright} 2018 Bentham Science Publishers. Background: Robust and accurate segmentation of the spine subdivided into individual vertebrae is necessary for subsequent diagnosis of illnesses related to the spine, particularly those requiring detection and classification of bone lesions. Based on correct vertebra segmentation, the current status of a disease under treatment-as well as its progress-can be determined and followed. Discussion: The problem is complicated by frequent heavy deformations of both the spine axis and individual vertebrae due to illness, so that some vertebrae may differ substantially from expected shapes or even be missing. This overview summarises and discusses so far published methods for spine and vertebrae segmentation in 3D CT thoracic data. Conclusion: It suggests a classification of these algorithms based on the used approaches, complexity of algorithms, as well as on achieved efficiencies. },
}

@article{Humbert2017,
  year = { 2017 },
  volume = { 36 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { 3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images },
  pages = { 27--39 },
  number = { 1 },
  keywords = { Bone mineral density,DXA,cortical thickness,image registration,osteoporosis,proximal femur },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 1558254X },
  doi = { 10.1109/TMI.2016.2593346 },
  author = { Humbert and Martelli and Fonolla and Steghofer and {DI Gregorio} and Malouf and Romera and Barquero },
  abstract = { The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the '3D-DXA' software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. },
}

@article{Hoinkiss2017,
  year = { 2017 },
  volume = { 78 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Prospective motion correction in 2D multishot MRI using EPI navigators and multislice-to-volume image registration },
  pages = { 2127--2135 },
  number = { 6 },
  keywords = { EPI navigator,RARE,motion artifacts,mutual information,prospective acquisition correction,real-time feedback },
  journal = { Magnetic Resonance in Medicine },
  issn = { 15222594 },
  doi = { 10.1002/mrm.26951 },
  author = { Hoinkiss and Porter },
  abstract = { Purpose: Prospective motion correction reduces artifacts in MRI by correcting for subject motion in real time, but techniques are limited for multishot 2-dimensional (2D) sequences. This study addresses this limitation by using 2D echo-planar imaging (EPI) slice navigator acquisitions together with a multislice-to-volume image registration. Methods: The 2D-EPI navigators were integrated into 2D imaging sequences to allow a rapid, real-time motion correction based on the registration of three navigator slices to a reference volume. A dedicated slice-iteration scheme was used to limit mutual spin-saturation effects between navigator and image data. The method was evaluated using T2-weighted spin echo and multishot rapid acquisition with relaxation enhancement (RARE) sequences, and its motion-correction capabilities were compared with those of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER). Validation was performed in vivo using a well-defined motion protocol. Results: Data acquired during subject motion showed residual motion parameters within ±0.5 mm and ±0.5°, and demonstrated a substantial improvement in image quality compared with uncorrected scans. In a comparison to PROPELLER, the proposed technique preserved a higher level of anatomical detail in the presence of subject motion. Conclusions: EPI-navigator-based prospective motion correction using multislice-to-volume image registration can substantially reduce image artifacts, while minimizing spin-saturation effects. The method can be adapted for use in other 2D MRI sequences and promises to improve image quality in routine clinical examinations. Magn Reson Med 78:2127–2135, 2017. {\textcopyright} 2017 International Society for Magnetic Resonance in Medicine. },
}

@article{Gustafsson2017,
  year = { 2017 },
  volume = { 62 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate },
  pages = { 2976--2989 },
  number = { 8 },
  keywords = { MRI only,MRI radiotherapy,MRI treatment planning,QA,distortion,synthetic CT },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/aa5fa2 },
  author = { Gustafsson and Nordstr{\"{o}}m and Persson and Brynolfsson and Olsson },
  abstract = { Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field. The displacement map for an optimized MRI treatment planning sequence was measured using a dedicated phantom in a 3 T MRI system. To simulate the distortion aspects of a synthetic CT (electron density derived from MR images), the displacement map was applied to CT images, referred to as distorted CT images. A volumetric modulated arc prostate treatment plan was applied to the original CT and the distorted CT, creating a reference and a distorted CT dose distribution. By applying the inverse of the displacement map to the distorted CT dose distribution, a dose distribution in the same geometry as the original CT images was created. For 10 prostate cancer patients, the dose difference between the reference dose distribution and inverse distorted CT dose distribution was analyzed in isodose level bins. The mean magnitude of the geometric distortion was 1.97 mm for the radial distance of 200-250 mm from isocenter. The mean percentage dose differences for all isodose level bins, were 0.02{\%} and the radiotherapy structure mean volume deviations were {\textless}0.2{\%}. The method developed can quantify the dosimetric effects of MRI system specific distortion in a prostate MRI only radiotherapy workflow, separated from dosimetric effects originating from synthetic CT generation. No clinically relevant dose difference or structure deformation was found when 3D distortion correction and high acquisition bandwidth was used. The method could be used for any MRI sequence together with any anatomy of interest. },
}

@article{Gillies2017,
  year = { 2017 },
  volume = { 44 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy: },
  pages = { 4708--4723 },
  number = { 9 },
  keywords = { 2D-3D transrectal ultrasound-guided prostate biops,prostate cancer,prostate motion compensation,real-time image registration },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.12441 },
  author = { Gillies and Gardi and {De Silva} and Zhao and Fenster },
  abstract = { Purpose: During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process. Methods: Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom. Results: After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding registration errors of 0.4 ± 0.3 mm, 0.2 ± 0.4 mm, and 0.8 ± 0.5. The continuous method performed registration significantly faster (P {\textless} 0.05) than the user initiated method, with observed computation times of 35 ± 8 ms, 43 ± 16 ms, and 27 ± 5 ms for in-plane, out-of-plane, and roll motions, respectively, and corresponding registration errors of 0.2 ± 0.3 mm, 0.7 ± 0.4 mm, and 0.8 ± 1.0. Conclusions: The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing {\textless} 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy. },
}

@article{DeLeener2017,
  year = { 2017 },
  volume = { 145 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data },
  pages = { 24--43 },
  keywords = { Atlas,MRI,Open-source,Software,Spinal cord,Template },
  journal = { NeuroImage },
  issn = { 10959572 },
  doi = { 10.1016/j.neuroimage.2016.10.009 },
  author = { {De Leener} and L{\'{e}}vy and Dupont and Fonov and Stikov and {Louis Collins} and Callot and Cohen-Adad },
  abstract = { For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures. },
}

@article{Ciardo2017,
  year = { 2017 },
  volume = { 32 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases },
  pages = { 44--52 },
  keywords = { Atlas-based segmentation,Automatic contouring,Breast cancer radiotherapy,STAPLE contours },
  journal = { Breast },
  issn = { 15323080 },
  doi = { 10.1016/j.breast.2016.12.010 },
  author = { Ciardo and Gerardi and Vigorito and Morra and Dell'acqua and Diaz and Cattani and Zaffino and Ricotti and Spadea and Riboldi and Orecchia and Baroni and Leonardi and Jereczek-Fossa },
  abstract = { Objectives Atlas-based automatic segmentation (ABAS) addresses the challenges of accuracy and reliability in manual segmentation. We aim to evaluate the contribution of specific-purpose in ABAS of breast cancer (BC) patients with respect to generic-purpose libraries. Materials and methods One generic-purpose and 9 specific-purpose libraries, stratified according to type of surgery and size of thorax circumference, were obtained from the computed tomography of 200 BC patients. Keywords about contralateral breast volume and presence of breast expander/prostheses were recorded. ABAS was validated on 47 independent patients, considering manual segmentation from scratch as reference. Five ABAS datasets were obtained, testing single-ABAS and multi-ABAS with simultaneous truth and performance level estimation (STAPLE). Center of mass distance (CMD), average Hausdorff distance (AHD) and Dice similarity coefficient (DSC) between corresponding ABAS and manual structures were evaluated and statistically significant differences between different surgeries, structures and ABAS strategies were investigated. Results Statistically significant differences between patients who underwent different surgery were found, with superior results for conservative-surgery group, and between different structures were observed: ABAS of heart, lungs, kidneys and liver was satisfactory (median values: CMD{\textless}2 mm, DSC≥0.80, AHD{\textless}1.5 mm), whereas chest wall, breast and spinal cord obtained moderate performance (median values: 2 mm ≤ CMD{\textless}5 mm, 0.60 ≤ DSC{\textless}0.80, 1.5 mm ≤ AHD{\textless}4 mm) and esophagus, stomach, brachial plexus and supraclavicular nodes obtained poor performance (median CMD≥5 mm, DSC{\textless}0.60, AHD≥4 mm). The application of STAPLE algorithm generally yields higher performance and the use of keywords improves results for breast ABAS. Conclusion The homogeneity in the selection of atlases based on multiple anatomical and clinical features and the use of specific-purpose libraries can improve ABAS performance with respect to generic-purpose libraries. },
}

@article{Acosta2017,
  year = { 2017 },
  volume = { 125 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy },
  pages = { 492--499 },
  number = { 3 },
  keywords = { Atlas-based segmentation,Dose computation,Prostate cancer radiotherapy,Urethra segmentation,Urinary toxicity },
  journal = { Radiotherapy and Oncology },
  issn = { 18790887 },
  doi = { 10.1016/j.radonc.2017.09.015 },
  author = { Acosta and Mylona and {Le Dain} and Voisin and Lizee and Rigaud and Lafond and Gnep and Crevoisier },
  abstract = { Background and purpose Segmentation of intra-prostatic urethra for dose assessment from planning CT may help explaining urinary toxicity in prostate cancer radiotherapy. This work sought to: i) propose an automatic method for urethra segmentation in CT, ii) compare it with previously proposed surrogate models and iii) quantify the dose received by the urethra in patients treated with IMRT. Materials and methods A weighted multi-atlas-based urethra segmentation method was devised from a training data set of 55 CT scans of patients receiving brachytherapy with visible urinary catheters. Leave-one-out cross validation was performed to quantify the error between the urethra segmentation and the catheter ground truth with two scores: the centerlines distance (CLD) and the percentage of centerline within a certain distance from the catheter (PWR). The segmentation method was then applied to a second test data set of 95 prostate cancer patients having received 78 Gy IMRT to quantify dose to the urethra. Results Mean CLD was 3.25 ± 1.2 mm for the whole urethra and 3.7 ± 1.7 mm, 2.52 ± 1.5 mm, and 3.01 ± 1.7 mm for the top, middle, and bottom thirds, respectively. In average, 53{\%} of the segmented centerlines were within a radius {\textless} 3.5 mm from the centerline ground truth and 83{\%} in a radius {\textless} 5 mm. The proposed method outperformed existing surrogate models. In IMRT, urethra DVH was significantly higher than prostate DVH from V74 Gy to V79 Gy. Conclusion A multi-atlas-based segmentation method was proposed enabling assessment of the dose within the prostatic urethra. },
}

@article{Grothausmann2017,
  year = { 2017 },
  volume = { 312 },
  url = { https://www.physiology.org/doi/10.1152/ajplung.00326.2016 },
  title = { Digital 3D reconstructions using histological serial sections of lung tissue including the alveolar capillary network },
  pages = { L243--L257 },
  number = { 2 },
  month = { feb },
  keywords = { 3D reconstruction,Alveolar capillary network (ACN),Stacked histological slices,Virtual endoscopy },
  journal = { American Journal of Physiology - Lung Cellular and Molecular Physiology },
  issn = { 15221504 },
  doi = { 10.1152/ajplung.00326.2016 },
  author = { Grothausmann and Knudsen and Ochs and M{\"{u}}hlfeld },
  abstract = { The alveolar capillary network (ACN) provides an enormously large surface area that is necessary for pulmonary gas exchange. Changes of the ACN during normal or pathological development or in pulmonary diseases are of great functional impact and warrant further analysis. Due to the complexity of the three-dimensional (3D) architecture of the ACN, 2D approaches are limited in providing a comprehensive impression of the characteristics of the normal ACN or the nature of its alterations. Stereological methods offer a quantitative way to assess the ACN in 3D in terms of capillary volume, surface area, or number but lack a 3D visualization to interpret the data. Hence, the necessity to visualize the ACN in 3D and to correlate this with data from the same set of data arises. Such an approach requires a large sample volume combined with a high resolution. Here, we present a technically simple and cost-efficient approach to create 3D representations of lung tissue ranging from bronchioles over alveolar ducts and alveoli up to the ACN from more than 1 mm sample extent to a resolution of less than 1 $\mu$m. The method is based on automated image acquisition of serially sectioned epoxy resin-embedded lung tissue fixed by vascular perfusion and subsequent automated digital reconstruction and analysis of the 3D data. This efficient method may help to better understand mechanisms of vascular development and pathology of the lung. },
}

@article{RN909,
  year = { 2017 },
  volume = { 2017 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030642114{\&}doi=10.1155{\%}2F2017{\%}2F2059036{\&}partnerID=40{\&}md5=c0598599370d8f954d410519826406e2 },
  type = { Journal Article },
  title = { Automatic Segmentation of Ultrasound Tomography Image },
  journal = { BioMed Research International },
  issn = { 23146141 },
  doi = { 10.1155/2017/2059036 },
  author = { Wu and Yu and Zhuang and Wei and Sak and Duric and Hu and Xie },
  abstract = { Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D=0.9275 and J=0.8660 and FP=0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification. },
}

@article{RN895,
  year = { 2017 },
  volume = { 77 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035021353{\&}doi=10.1158{\%}2F0008-5472.CAN-17-0339{\&}partnerID=40{\&}md5=e2bbcfe2a2e6a8ec00b02cf00ef7f2fa },
  type = { Journal Article },
  title = { Computational radiomics system to decode the radiographic phenotype },
  pmid = { 29092951 },
  pages = { e104--e107 },
  number = { 21 },
  journal = { Cancer Research },
  issn = { 15387445 },
  doi = { 10.1158/0008-5472.CAN-17-0339 },
  author = { {Van Griethuysen} and Fedorov and Parmar and Hosny and Aucoin and Narayan and Beets-Tan and Fillion-Robin and Pieper and Aerts },
  abstract = { Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www. radiomics.io. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. },
}

@article{RN899,
  year = { 2017 },
  volume = { 4 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032914246{\&}doi=10.1117{\%}2F1.JMI.4.4.044002{\&}partnerID=40{\&}md5=c806ed79840aa4e089be7a24062199ec },
  type = { Journal Article },
  title = { Mesh-to-raster region-of-interest-based nonrigid registration of multimodal images },
  pages = { 1 },
  number = { 04 },
  journal = { Journal of Medical Imaging },
  issn = { 2329-4310 },
  eprint = { 1703.01972 },
  doi = { 10.1117/1.jmi.4.4.044002 },
  author = { Tatano and Berkels and Deserno },
  arxivid = { 1703.01972 },
  archiveprefix = { arXiv },
  abstract = { Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix. },
}

@article{RN800,
  year = { 2017 },
  volume = { 62 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy },
  pages = { 3065--3080 },
  number = { 8 },
  keywords = { Bayesian,intrafraction imaging,lung cancer,markerless,tumor tracking },
  journal = { Physics in Medicine and Biology },
  issn = { 13616560 },
  doi = { 10.1088/1361-6560/aa6393 },
  author = { Shieh and Caillet and Dunbar and Keall and Booth and Hardcastle and Haddad and Eade and Feain },
  abstract = { The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with {\textgreater}10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude {\textless}5 mm was 96.5{\%} for markerless tumor tracking and 84.1{\%} for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method. },
}

@article{RN892,
  year = { 2017 },
  volume = { 44 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037856250{\&}doi=10.1002{\%}2Fmp.12586{\&}partnerID=40{\&}md5=e15c5e0e2cbe8975290ae3d911a44b52 },
  type = { Journal Article },
  title = { Voxel-based multimodel fitting method for modeling time activity curves in SPECT images: },
  pages = { 6280--6288 },
  number = { 12 },
  keywords = { SPECT,dosimetry,targeted radionuclide therapy,time activity curve,voxel-based },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1002/mp.12586 },
  author = { Sarrut and Halty and Badel and Ferrer and Bardi{\`{e}}s },
  abstract = { Purpose: Estimating the biodistribution and the pharmacokinetics from time-sequence SPECT images on a per-voxel basis is useful for studying activity nonuniformity or computing absorbed dose distributions by convolution of voxel kernels or Monte-Carlo radiation transport. Current approaches are either region-based, thus assuming uniform activity within the region, or voxel-based but using the same fitting model for all voxels. Methods: We propose a voxel-based multimodel fitting method (VoMM) that estimates a fitting function for each voxel by automatically selecting the most appropriate model among a predetermined set with Akaike criteria. This approach can be used to compute the time integrated activity (TIA) for all voxels in the image. To control fitting optimization that may fail due to excessive image noise, an approximated version based on trapezoid integration, named restricted method, is also studied. From this comparison, the number of failed fittings within images was estimated and analyzed. Numerical experiments were used to quantify uncertainties and feasibility was demonstrated with real patient data. Results: Regarding numerical experiments, root mean square errors of TIA obtained with VoMM were similar to those obtained with bi-exponential fitting functions, and were lower ({\textless} 5{\%} vs. {\textgreater} 10{\%}) than with single model approaches that consider the same fitting function for all voxels. Failure rates were lower with VoMM and restricted approaches than with single-model methods. On real clinical data, VoMM was able to fit 90{\%} of the voxels and led to less failed fits than single-model approaches. On regions of interest (ROI) analysis, the difference between ROI-based and voxel-based TIA estimations was low, less than 4{\%}. However, the computation of the mean residence time exhibited larger differences, up to 25{\%}. Conclusions: The proposed voxel-based multimodel fitting method, VoMM, is feasible on patient data. VoMM leads organ-based TIA estimations similar to conventional ROI-based method. However, for pharmacokinetics analysis, studies of spatial heterogeneity or voxel-based absorbed dose assessment, VoMM could be used preferentially as it prevents model overfitting. },
}

@article{RN893,
  year = { 2017 },
  volume = { 78 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026850018{\&}doi=10.1055{\%}2Fs-0037-1604406{\&}partnerID=40{\&}md5=e63320b0921b5580303ada6248b2e769 },
  type = { Journal Article },
  title = { Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data },
  pages = { 490--496 },
  number = { 6 },
  keywords = { anatomical region,atlas-based segmentation,motion analysis,objective skill assessment,operating room data,sinus surgery,skull base },
  journal = { Journal of Neurological Surgery, Part B: Skull Base },
  issn = { 21936331 },
  doi = { 10.1055/s-0037-1604406 },
  author = { Li and Bly and Harbison and Humphreys and Whipple and Hannaford and Moe },
  abstract = { Background Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities. },
}

@article{RN827,
  year = { 2017 },
  volume = { 11 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Simulating longitudinal brain MRIs with known volume changes and realistic variations in image intensity },
  number = { MAR },
  keywords = { Biomechanical simulation,Biophysical modeling,Neurodegeneration,Simulated database,Synthetic images,Synthetic longitudinal MRIs },
  journal = { Frontiers in Neuroscience },
  issn = { 1662453X },
  doi = { 10.3389/fnins.2017.00132 },
  author = { Khanal and Ayache and Pennec },
  abstract = { This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation. },
}

@article{RN798,
  year = { 2017 },
  volume = { 47 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Image-based immersed boundary model of the aortic root },
  pages = { 72--84 },
  keywords = { Aortic valve,Finite element method,Fluid–structure interaction,Immersed boundary method,Nonlinear elasticity },
  journal = { Medical Engineering and Physics },
  issn = { 18734030 },
  eprint = { 1705.04279 },
  doi = { 10.1016/j.medengphy.2017.05.007 },
  author = { Hasan and Kolahdouz and Enquobahrie and Caranasos and Vavalle and Griffith },
  arxivid = { 1705.04279 },
  archiveprefix = { arXiv },
  abstract = { Each year, approximately 300,000 heart valve repair or replacement procedures are performed worldwide, including approximately 70,000 aortic valve replacement surgeries in the United States alone. Computational platforms for simulating cardiovascular devices such as prosthetic heart valves promise to improve device design and assist in treatment planning, including patient-specific device selection. This paper describes progress in constructing anatomically and physiologically realistic immersed boundary (IB) models of the dynamics of the aortic root and ascending aorta. This work builds on earlier IB models of fluid–structure interaction (FSI) in the aortic root, which previously achieved realistic hemodynamics over multiple cardiac cycles, but which also were limited to simplified aortic geometries and idealized descriptions of the biomechanics of the aortic valve cusps. By contrast, the model described herein uses an anatomical geometry reconstructed from patient-specific computed tomography angiography (CTA) data, and employs a description of the elasticity of the aortic valve leaflets based on a fiber-reinforced constitutive model fit to experimental tensile test data. The resulting model generates physiological pressures in both systole and diastole, and yields realistic cardiac output and stroke volume at physiological Reynolds numbers. Contact between the valve leaflets during diastole is handled automatically by the IB method, yielding a fully competent valve model that supports a physiological diastolic pressure load without regurgitation. Numerical tests show that the model is able to resolve the leaflet biomechanics in diastole and early systole at practical grid spacings. The model is also used to examine differences in the mechanics and fluid dynamics yielded by fresh valve leaflets and glutaraldehyde-fixed leaflets similar to those used in bioprosthetic heart valves. Although there are large differences in the leaflet deformations during diastole, the differences in the open configurations of the valve models are relatively small, and nearly identical hemodynamics are obtained in all cases considered. },
}

@article{RN891,
  year = { 2017 },
  volume = { 116 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040316780{\&}doi=10.1002{\%}2Fjso.24788{\&}partnerID=40{\&}md5=759b8bc9fcaf202c7bea3274c812f4c2 },
  type = { Journal Article },
  title = { MRI to predict nipple-areola complex (NAC) involvement: An automatic method to compute the 3D distance between the NAC and tumor },
  pages = { 1069--1078 },
  number = { 8 },
  keywords = { 3D automatic distance,breast cancer,magnetic resonance imaging,mastectomy,nipple-areola sparing,tumor segmentation },
  journal = { Journal of Surgical Oncology },
  issn = { 10969098 },
  doi = { 10.1002/jso.24788 },
  author = { Giannini and Bianchi and Carabalona and Mazzetti and Maggiorotto and Kubatzki and Regge and Ponzone and Martincich },
  abstract = { Objectives: To assess the role in predicting nipple-areola complex (NAC) involvement of a newly developed automatic method which computes the 3D tumor-NAC distance. Patients and Methods: Ninety-nine patients scheduled to nipple sparing mastectomy (NSM) underwent magnetic resonance (MR) examination at 1.5 T, including sagittal T2w and dynamic contrast enhanced (DCE)-MR imaging. An automatic method was developed to segment the NAC and the tumor and to compute the 3D distance between them. The automatic measurement was compared with manual axial and sagittal 2D measurements. NAC involvement was defined by the presence of invasive ductal or lobular carcinoma and/or ductal carcinoma in situ or ductal intraepithelial neoplasia (DIN1c − DIN3). Results: Tumor-NAC distance was computed on 95/99 patients (25 NAC+), as three tumors were not correctly segmented (sensitivity = 97{\%}), and 1 NAC was not detected (sensitivity = 99{\%}). The automatic 3D distance reached the highest area under the receiver operating characteristic (ROC) curve (0.830) with respect to the manual axial (0.676), sagittal (0.664), and minimum distances (0.664). At the best cut-off point of 21 mm, the 3D distance obtained sensitivity = 72{\%}, specificity = 80{\%}, positive predictive value = 56{\%}, and negative predictive value = 89{\%}. Conclusions: This method could provide a reproducible biomarker to preoperatively select breast cancer patients candidates to NSM, thus helping surgical planning and intraoperative management of patients. },
}

@article{RN903,
  year = { 2017 },
  volume = { 32 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013042443{\&}doi=10.1002{\%}2Fjbmr.3084{\&}partnerID=40{\&}md5=18906d1bf5b2d3d0e94d19b1af8384bf },
  type = { Journal Article },
  title = { Adaptations in the Microarchitecture and Load Distribution of Maternal Cortical and Trabecular Bone in Response to Multiple Reproductive Cycles in Rats },
  pages = { 1014--1026 },
  number = { 5 },
  keywords = { BONE FORMATION,BONE MICROARCHITECTURE,LACTATION,PREGNANCY,STIFFNESS,WEANING },
  journal = { Journal of Bone and Mineral Research },
  issn = { 15234681 },
  doi = { 10.1002/jbmr.3084 },
  author = { Bakker and Altman-Singles and Li and Tseng and Li and Liu },
  abstract = { Pregnancy, lactation, and weaning result in dramatic changes in maternal calcium metabolism. In particular, the increased calcium demand during lactation causes a substantial degree of maternal bone loss. This reproductive bone loss has been suggested to be largely reversible, as multiple clinical studies have found that parity and lactation history have no adverse effect on postmenopausal fracture risk. However, the precise effects of pregnancy, lactation, and post-weaning recovery on maternal bone structure are not well understood. Our study aimed to address this question by longitudinally tracking changes in trabecular and cortical bone microarchitecture at the proximal tibia in rats throughout three cycles of pregnancy, lactation, and post-weaning using in vivo $\mu$CT. We found that the trabecular thickness underwent a reversible deterioration during pregnancy and lactation, which was fully recovered after weaning, whereas other parameters of trabecular microarchitecture (including trabecular number, spacing, connectivity density, and structure model index) underwent a more permanent deterioration, which recovered minimally. Thus, pregnancy and lactation resulted in both transient and long-lasting alterations in trabecular microstructure. In the meantime, multiple reproductive cycles appeared to improve the robustness of cortical bone (resulting in an elevated cortical area and polar moment of inertia), as well as increase the proportion of the total load carried by the cortical bone at the proximal tibia. Taken together, changes in the cortical and trabecular compartments suggest that whereas rat tibial trabecular bone appears to be highly involved in maintaining calcium homeostasis during female reproduction, cortical bone adapts to increase its load-bearing capacity, allowing the overall mechanical function of the tibia to be maintained. {\textcopyright} 2017 American Society for Bone and Mineral Research. },
}

@article{johnson2015itk,
  year = { 2017 },
  url = { https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2.html },
  title = { The ITK Software Guide Book 2: Design and Functionality },
  publisher = { Kitware, Inc. },
  keywords = { Guide,Registration,Segmentation },
  journal = { Itk },
  isbn = { 978-1-930934-28-3 },
  doi = { 1�930934-15�7 },
  author = { Johnson and Ib and Mccormick and {Insight Software Consortium.} },
}

@incollection{RN956,
  year = { 2017 },
  volume = { 227 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030665154{\&}doi=10.1007{\%}2F978-3-319-56895-9{\_}8{\&}partnerID=40{\&}md5=ee50a7c74dbc3a51f07be9c4038bb7b0 },
  type = { Serial },
  title = { Analysis, recognition, and classification of biological membrane images },
  publisher = { Springer Verlag },
  pages = { 119--140 },
  issn = { 03015556 },
  isbn = { 03015556 (ISSN) },
  doi = { 10.1007/978-3-319-56895-9_8 },
  booktitle = { Advances in Anatomy Embryology and Cell Biology },
  author = { Kulbacki and Segen and Bak },
  abstract = { Biological membrane images contain a variety of objects and patterns, which convey information about the underlying biological structures and mechanisms. The field of image analysis includes methods of computation which convert features and objects identified in images into quantitative information about biological structures represented in these images. Microscopy images are complex, noisy, and full of artifacts and consequently require multiple image processing steps for the extraction of meaningful quantitative information. This review is focused on methods of analysis of images of cells and biological membranes such as detection, segmentation, classification and machine learning, registration, tracking, and visualization. These methods could make possible, for example, to automatically identify defects in the cell membrane which affect physiological processes. Detailed analysis of membrane images could facilitate understanding of the underlying physiological structures or help in the interpretation of biological experiments. },
}

@inproceedings{Wetzel,
  year = { 2017 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028755924{\&}doi=10.1109{\%}2FAIPR.2016.8010595{\&}partnerID=40{\&}md5=688016e1bcc150f30e8230da73803097 },
  type = { Conference Proceedings },
  title = { Registering large volume serial-section electron microscopy image sets for neural circuit reconstruction using FFT signal whitening },
  keywords = { connectomics,electron microscopy,image registration,neural circuit reconstruction,signal whitening },
  issn = { 21642516 },
  isbn = { 9781509032846 },
  eprint = { 1612.04787 },
  doi = { 10.1109/AIPR.2016.8010595 },
  booktitle = { Proceedings - Applied Imagery Pattern Recognition Workshop },
  author = { Wetzel and Bakal and Dittrich and Hildebrand and Morgan and Lichtman },
  arxivid = { 1612.04787 },
  archiveprefix = { arXiv },
  abstract = { The detailed reconstruction of neural anatomy for connectomics studies requires a combination of resolution and large three-dimensional data capture provided by serial section electron microscopy (ssEM). The convergence of high throughput ssEM imaging and improved tissue preparation methods now allows ssEM capture of complete specimen volumes up to cubic millimeter scale. The resulting multi-terabyte image sets span thousands of serial sections and must be precisely registered into coherent volumetric forms in which neural circuits can be traced and segmented. This paper introduces a Signal Whitening Fourier Transform Image Registration approach (SWiFT-IR) under development at the Pittsburgh Supercomputing Center and its use to align mouse and zebrafish brain datasets acquired using the wafer mapper ssEM imaging technology recently developed at Harvard University. Unlike other methods now used for ssEM registration, SWiFT-IR modifies its spatial frequency response during image matching to maximize a signal-to-noise measure used as its primary indicator of alignment quality. This alignment signal is more robust to rapid variations in biological content and unavoidable data distortions than either phase-only or standard Pearson correlation, thus allowing more precise alignment and statistical confidence. These improvements in turn enable an iterative registration procedure based on projections through multiple sections rather than more typical adjacent-pair matching methods. This projection approach, when coupled with known anatomical constraints and iteratively applied in a multi-resolution pyramid fashion, drives the alignment into a smooth form that properly represents complex and widely varying anatomical content such as the full crosssection zebrafish data. },
}

@inproceedings{RN976,
  year = { 2017 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041527605{\&}doi=10.2352{\%}2FISSN.2470-1173.2017.2.VIPC-404{\&}partnerID=40{\&}md5=611236c5db623b23e4984bfc71e20bb2 },
  type = { Conference Proceedings },
  title = { A fast and accurate segmentation method for medical images },
  publisher = { Society for Imaging Science and Technology },
  pages = { 38--43 },
  issn = { 24701173 },
  isbn = { 24701173 (ISSN) },
  editor = { [object Object],[object Object] },
  doi = { 10.2352/ISSN.2470-1173.2017.2.VIPC-404 },
  booktitle = { IS and T International Symposium on Electronic Imaging Science and Technology },
  author = { Wu and Li and Peng and Fan },
  abstract = { Selecting regions of interest (ROI) of the medical images is an important task in medical image processing. Manual selection of ROIs serves as the main method for single images and it has a high accuracy. However, it will become infeasible to manually segment ROIs on a large number of images. Observing this problem, this paper proposes a fast and accurate segmentation method to obtain ROIs on a batch of medical images. Firstly, we segment the standard brain image St which has not been injected with tracer. Secondly, we use a B-Spline elastic registration method to get the inverse-registration parameters. Thirdly, we get the template image Te with the registration parameters. Finally, we search the target region by template matching. Experimental results show that the proposed method performs well on medical image segmentation. },
}

@inproceedings{RN896,
  year = { 2017 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039446737{\&}doi=10.1109{\%}2FULTSYM.2017.8092085{\&}partnerID=40{\&}md5=6bb0c6a43db0421599b9f25e0926d036 },
  type = { Conference Proceedings },
  title = { Fast multilevel Lagrangian carotid strain imaging with GPU computing },
  publisher = { IEEE Computer Society },
  keywords = { CUDA,Carotid Plaque,GPU,Lagrangian,Strain Imaging,Ultrasound },
  issn = { 19485727 },
  isbn = { 9781538633830 },
  doi = { 10.1109/ULTSYM.2017.8092085 },
  booktitle = { IEEE International Ultrasonics Symposium, IUS },
  author = { Meshram and Varghese },
  abstract = { Lagrangian carotid strain imaging (LCSI) involves estimation of deformation in the carotid artery due to blood pressure variations under cardiac pulsation. Local strain over a cardiac cycle is tracked, which is computationally intensive. We incur long offline processing times for LCSI which becomes a limiting factor for clinical adoption. We report on the computational speedup obtained for a parallelized implementation of LCSI using CUDA programming for fast computation. LCSI is currently performed using a multi-level block matching algorithm written in C++ using the Insight Toolkit (ITK) system. We have implemented this code on a NVIDIA k40 GPU for running CUDA kernels called from the ITK C++ code. The multi-level algorithm consists of three processing stages; stage 1 performs block matching at the coarsest level while level 3 performs block-matching at the finest scale on radiofrequency signals. The regularization step which incurred the largest computational time was implemented on the GPU. Cross-correlation was then implemented with the regularization step thereby avoiding a CPU to GPU data transfer. Shared memory was used in the regularization step to further reduce processing time. The computation time per frame pairs for LCSI with our initial implementation was about 316.41 secs for an in vivo human carotid data set, thereby taking 131 minutes for an entire loop over a cardiac cycle with 25 frames. GPU implementation of regularization provided per frame results in 99.92 secs, a speedup of 3.16X. Further optimization with implementation of cross correlation on the GPU and use of shared memory improved the computation time to 23 secs per frame, a speed up of 13.75X, reducing processing time to 9.5 minutes over a cardiac cycle. },
}

@inproceedings{RN963,
  year = { 2017 },
  volume = { 10135 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020389194{\&}doi=10.1117{\%}2F12.2254678{\&}partnerID=40{\&}md5=6fd25bdbdca508f33b2b3846f0f0d7fc },
  type = { Conference Proceedings },
  title = { 3D/2D image registration method for joint motion analysis using low-quality images from mini C-arm machines },
  publisher = { SPIE },
  pages = { 101350B },
  issn = { 16057422 },
  isbn = { 9781510607156 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2254678 },
  booktitle = { Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling },
  author = { Ghafurian and Hacihaliloglu and Metaxas and Tan and Li },
  abstract = { {\textcopyright} 2017 SPIE. A 3D kinematic measurement of joint movement is crucial for orthopedic surgery assessment and diagnosis. This is usually obtained through a frame-by-frame registration of the 3D bone volume to a uoroscopy video of the joint movement. The high cost of a high-quality uoroscopy imaging system has hindered the access of many labs to this application. This is while the more affordable and low-dosage version, the mini C-Arm, is not commonly used for this application due to low image quality. In this paper, we introduce a novel method for kinematic analysis of joint movement using the mini C-Arm. In this method the bone of interest is recovered and isolated from the rest of the image using a non-rigid registration of an atlas to each frame. The 3D/2D registration is then performed using the weighted histogram of image gradients as an image feature. In our experiments, the registration error was 0.89 mm and 2.36°for human C2 vertebra. While the precision is still lacking behind a high quality uoroscopy machine, it is a good starting point facilitating the use of mini C-Arms for motion analysis making this application available to lower-budget environments. Moreover, the registration was highly resistant to the initial distance from the true registration, converging to the answer from anywhere within ±90°of it. },
}

@book{Higham2016,
  year = { 2016 },
  title = { MATLAB Guide, Third Edition },
  doi = { 10.1137/1.9781611974669 },
  booktitle = { MATLAB Guide, Third Edition },
  author = { Higham and Higham },
  abstract = { Third edition. "MATLAB is an interactive system for numerical computation that is widely used for teaching and research in industry and academia. It provides a modern programming language and problem solving environment, with powerful data structures, customizable graphics, and easy-to-use editing and debugging tools. This third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 25 percent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox. Key features are a tutorial in Chapter 1 that gives a hands-on overview of MATLAB, a thorough treatment of MATLAB mathematics, including the linear algebra and numerical analysis functions and the differential equation solvers, and a web page that provides a link to example program files, updates, and links to MATLAB resources. The new edition contains color figures throughout, includes pithy discussions of related topics in new "Asides" boxes that augment the text, has new chapters on the Parallel Computing Toolbox, object-oriented programming, graphs, and large data sets, covers important new MATLAB data types such as categorical arrays, string arrays, tall arrays, tables, and timetables, contains more on MATLAB workflow, including the Live Editor and unit tests, and fully reflects major updates to the MATLAB graphics system." Chapter 1: A Brief Tutorial -- Chapter 2: Basics -- Chapter 3: Distinctive Features of MATLAB -- Chapter 4: Arithmetic -- Chapter 5: Matrices -- Chapter 6: Operators and Flow Control -- Chapter 7: Program Files -- Chapter 8: Graphics -- Chapter 9: Linear Algebra -- Chapter 10: More on Functions -- Chapter 11: Numerical Methods: Part I -- Chapter 12: Numerical Methods: Part II -- Chapter 13: Input and Output -- Chapter 14: Troubleshooting -- Chapter 15: Sparse Matrices -- Chapter 16: More on Coding -- Chapter 17: Advanced Graphics -- Chapter 18: Other Data Types and Multidimensional Arrays -- Chapter 19: Object-Oriented Programming -- Chapter 20: The Symbolic Math Toolbox -- Chapter 21: Graphs -- Chapter 22: Large Data Sets -- Chapter 23: Optimizing Codes -- Chapter 24: Tricks and Tips -- Chapter 25: The Parallel Computing Toolbox -- Chapter 26: Case Studies. },
}

@inbook{ElNaqa2016,
  year = { 2016 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018895187{\&}doi=10.1007{\%}2F978-3-319-40070-9{\_}12{\&}partnerID=40{\&}md5=a2572f421a96f250c396c7c0538f189d },
  type = { Book Section },
  title = { Image processing and analysis of PET and hybrid PET imaging },
  pages = { 285--301 },
  keywords = { Hybrid imaging,Image processing,Quantitative PET,Radiomics },
  isbn = { 9783319400709 },
  doi = { 10.1007/978-3-319-40070-9_12 },
  booktitle = { Basic Science of PET Imaging },
  author = { {El Naqa} },
  abstract = { PET imaging is a main diagnostic modality of different diseases including cancer. In the particular case of cancer, PET is widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, PET has benefited from early adoption of quantitative image analysis starting from simple standard uptake value (SUV) normalization to more advanced extraction of complex imaging uptake patterns, thanks chiefly to the application of sophisticated image processing algorithms. In this chapter, we discuss the application of image processing techniques to PET imaging with special focus on the oncological radiotherapy domain starting from basic feature extraction to application in target definition using image segmentation/registration and more recent image-based outcome modeling in the radiomics field. We further extend the discussion into hybrid anatomical functional combinations of PET/CT and PET/MR multimodalities. },
}

@article{forbes2016open,
  year = { 2016 },
  volume = { 10 },
  url = { http://journal.frontiersin.org/Article/10.3389/fninf.2016.00029/abstract{\%}5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/27536233{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4971025 },
  title = { An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. },
  publisher = { Frontiers Media SA },
  pmid = { 27536233 },
  pages = { 1--11 },
  month = { aug },
  keywords = { Huntingtons Disease,ITK,brain MRI,label atlas,multi-atlas,multi-modal,open-source },
  journal = { Frontiers in neuroinformatics },
  issn = { 1662-5196 },
  doi = { 10.3389/fninf.2016.00029 },
  author = { Forbes and Kim and Paulsen and Johnson },
  annote = { \#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\# },
  abstract = { The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48{\%}. },
}

@article{Zaffino2016,
  year = { 2016 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Technical Note: Plastimatch mabs, an open source tool for automatic image segmentation },
  pages = { 5155--5160 },
  number = { 9 },
  keywords = { CT,MRI,automatic segmentation,multiatlas based segmentation,open source },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4961121 },
  author = { Zaffino and Raudaschl and Fritscher and Sharp and Spadea },
  abstract = { Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce PLASTIMATCH MABS, an open source software that can be used with any image modality for automatic segmentation. Methods: PLASTIMATCH MABS workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H{\&}N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against other segmentation algorithms. },
}

@article{Tong2016,
  year = { 2016 },
  volume = { 36 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration },
  pages = { S127--S136 },
  number = { 12 },
  keywords = { Age-related macular degeneration,Autofluorescence,Bruch membrane,Drusen,Fluorophores,Hyperspectral imaging,Lipofuscin,Nonnegative tensor factorization,Retinal pigment epithelium,Sub-retinal pigment epithelium deposits },
  journal = { Retina },
  issn = { 15392864 },
  doi = { 10.1097/IAE.0000000000001325 },
  author = { Tong and Ami and Hong and Heintzmann and Gerig and Ablonczy and Curcio and Ach and Smith },
  abstract = { Purpose: To elucidate the molecular pathogenesis of age-related macular degeneration (AMD) and interpretation of fundus autofluorescence imaging, the authors identified spectral autofluorescence characteristics of drusen and retinal pigment epithelium (RPE) in donor eyes with AMD. Methods: Macular RPE/Bruch membrane flat mounts were prepared from 5 donor eyes with AMD. In 12 locations (1-3 per eye), hyperspectral autofluorescence images in 10-nmwavelength steps were acquired at 2 excitation wavelengths (lex 436, 480 nm). A nonnegative tensor factorization algorithm was used to recover 5 abundant emission spectra and their corresponding spatial localizations. Results: At lex 436 nm, the authors consistently localized a novel spectrum (SDr) with a peak emission near 510 nm in drusen and sub-RPE deposits. Abundant emission spectra seen previously (S0 in Bruch membrane and S1, S2, and S3 in RPE lipofuscin/melanolipofuscin, respectively) also appeared in AMD eyes, with the same shapes and peak wavelengths as in normal tissue. Lipofuscin/melanolipofuscin spectra localizations in AMD eyes varied widely in their overlap with drusen, ranging from none to complete. Conclusion: An emission spectrum peaking at-510 nm (lex 436 nm) appears to be sensitive and specific for drusen and sub-RPE deposits. One or more abundant spectra from RPE organelles exhibit characteristic relationships with drusen. },
}

@article{Stoyanova2016,
  year = { 2016 },
  volume = { 5 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Prostate cancer radiomics and the promise of radiogenomics },
  pmid = { 29188191 },
  pages = { 432--447 },
  number = { 4 },
  keywords = { Gene expression,MRI-targeted biopsies,Multiparametric MRI,Prostate cancer,Radiogenomics,Radiomics },
  journal = { Translational Cancer Research },
  issn = { 22196803 },
  doi = { 10.21037/tcr.2016.06.20 },
  author = { Stoyanova and Takhar and Tschudi and Ford and Sol{\'{o}}rzano and Erho and Balagurunathan and Punnen and Davicioni and Gillies and Pollack },
  abstract = { Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. },
}

@article{Ozbolat2016,
  year = { 2016 },
  volume = { 3 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016144668{\&}doi=10.1016{\%}2Fj.bprint.2016.11.001{\&}partnerID=40{\&}md5=be8ae863546f5861c3ed0df09aaee711 },
  type = { Journal Article },
  title = { A review on design for bioprinting },
  pages = { 1--14 },
  keywords = { Blueprint modeling,Design requirements for bioprinting,Medical imaging,Toolpath planning },
  journal = { Bioprinting },
  issn = { 24058866 },
  doi = { 10.1016/j.bprint.2016.11.001 },
  author = { Ozbolat and Gudapati },
  abstract = { In order to bioprint living tissue and organ constructs, patient-specific anatomical models need to be acquired; however, these models mainly provide external surface information only. The internal architecture of tissue constructs plays a crucial role as it provides a porous environment for media exchange, vascularization, tissue growth and engraftment. This review presents design requirements for bioprinting and discusses currently available medical imaging techniques used in acquisition of anatomical models including magnetic resonance imaging (MRI) and computed tomography (CT), and compares their strengths and limitations. Then, consideration for design architecture is discussed and various approaches in blueprint modeling of tissue constructs are presented for creation of porous architectures. Next, existing toolpath planning approaches for bioprinting of tissues and organs are presented. Design limitations for bioprinting are discussed and future perspectives are provided to the reader. },
}

@article{Mukherjee2016,
  year = { 2016 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Improved frame-based estimation of head motion in PET brain imaging },
  pages = { 2443--2454 },
  number = { 5 },
  keywords = { PET,motion compensation,motion tracking,reconstruction,registration },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4946814 },
  author = { Mukherjee and Lindsay and Mukherjee and Olivier and Shao and King and Licho },
  abstract = { Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type. },
}

@article{Lan2016,
  year = { 2016 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications? },
  pages = { 5072--5079 },
  number = { 9 },
  keywords = { non-small cell lung cancer,radiation fibrosis,regional ventilation function },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4960367 },
  author = { Lan and Jeudy and Senan and {Van Sornsen De Koste} and D'Souza and Tseng and Zhou and Zhang },
  abstract = { Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatingemcitabine, followed by two to three cycles of cisplatinetoposide concurrently with involved-field thoracic radiotherapy (4666 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density changebased algorithm with mass correction. In addition to the conventional dosevolume metrics (V20, V30, V40, and mean lung dose), dosefunction metrics (fV20, fV30, fV40, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dosesubvolume metrics (sV20, sV30, sV40, and subvolume mean lung dose); these were defined as the conventional dosevolume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dosevolume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.650.69, 0.570.70, and 0.690.76, respectively. The respective ranges for dosefunction metrics were 0.630.66, 0.610.71, and 0.720.80 and for dosesubvolume metrics were 0.500.65, 0.650.75, and 0.730.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dosevolume, dosefunction, dosesubvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dosefunction and dosesubvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dosefunction and/or dosesubvolume metrics with dosevolume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dosesubvolume metrics provided a promising method for preventing radiation-induced pulmonary complications. },
}

@article{Humbert2016,
  year = { 2016 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship },
  pages = { 1945--1954 },
  number = { 4 },
  keywords = { bone mineral density,computed tomography,cortical thickness,hip fracture,osteoporosis },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4944501 },
  author = { Humbert and {Hazrati Marangalou} and {Del R{\'{i}}o Barquero} and {Van Lenthe} and {Van Rietbergen} },
  abstract = { Purpose: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. Methods: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. Results: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (-18 ± 92 mg/cm3) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm3), and 0.10 ± 0.24 mm (-10 ± 115 mg/cm3) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm3). A trend for the cortical thickness and density estimation errors to increase with voxel size was observed and was more pronounced for thin cortices. Using clinical CT data for 19 of the 23 samples, mean errors of 0.18 ± 0.24 mm for the cortical thickness and 15 ± 106 mg/cm3 for the density were found. The case-control study showed that osteoporotic patients had a thinner cortex and a lower cortical density, with average differences of -0.8 mm and -58.6 mg/cm3 at the proximal femur in comparison with age-matched controls (p-value {\textless} 0.001). Conclusions: This method might be a promising approach for the quantification of cortical bone thickness and density using clinical routine imaging techniques. Future work will concentrate on investigating how this approach can improve the estimation of mechanical strength of bony structures, the prevention of fracture, and the management of osteoporosis. },
}

@article{Herrmann2016,
  year = { 2016 },
  volume = { 51 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Methods for fibre orientation analysis of X-ray tomography images of steel fibre reinforced concrete (SFRC) },
  pages = { 3772--3783 },
  number = { 8 },
  journal = { Journal of Materials Science },
  issn = { 15734803 },
  doi = { 10.1007/s10853-015-9695-4 },
  author = { Herrmann and Pastorelli and Kallonen and Suuronen },
  abstract = { One of the most important factors to determine the mechanical properties of a fibre composite material is the orientation of the fibres in the matrix. This paper presents Hessian matrix-based algorithms to retrieve the orientation of individual fibres out of steel fibre reinforced cementitious composites samples scanned with an X-ray computed tomography scanner. The software implemented with the algorithms includes a massive data filtering component to remove noise from the data-sets and prepare them correctly for the analysis. Due to its short computational times and limited need for user intervention, the software is able to process and analyse large batches of data in short periods and provide results in a variety of visual and numerical formats. The application and comparison of these algorithms lead to further insight into the material behaviour. In contrast to the usual assumption that the fibres act only along their main axis, it is shown that the contribution of hooked-end fibres in other directions may be noticeable. This means that fibres, depending on their shape, should act as orthotropic inclusions. The methods can be used by research laboratories and companies on an everyday basis to obtain fibre orientations from samples, which in turn can be used in research, to study stress–strain behaviour, as input to constitutive models or for quality assurance. },
}

@article{FennemaNotestine2016,
  year = { 2016 },
  volume = { 12 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { White matter disease in midlife is heritable, related to hypertension, and shares some genetic influence with systolic blood pressure },
  pages = { 737--745 },
  keywords = { Blood pressure,Brain,Heritability,Hypertension,MRI,White matter },
  journal = { NeuroImage: Clinical },
  issn = { 22131582 },
  doi = { 10.1016/j.nicl.2016.10.001 },
  author = { Fennema-Notestine and McEvoy and Notestine and Panizzon and Yau and Franz and Lyons and Eyler and Neale and Xian and McKenzie and Kremen },
  abstract = { White matter disease in the brain increases with age and cardiovascular disease, emerging in midlife, and these associations may be influenced by both genetic and environmental factors. We examined the frequency, distribution, and heritability of abnormal white matter and its association with hypertension in 395 middle-aged male twins (61.9 ± 2.6 years) from the Vietnam Era Twin Study of Aging, 67{\%} of whom were hypertensive. A multi-channel segmentation approach estimated abnormal regions within the white matter. Using multivariable regression models, we characterized the frequency distribution of abnormal white matter in midlife and investigated associations with hypertension and Apolipoprotein E-$\epsilon$4 status and the impact of duration and control of hypertension. Then, using the classical twin design, we estimated abnormal white matter heritability and the extent of shared genetic overlap with blood pressure. Abnormal white matter was predominantly located in periventricular and deep parietal and frontal regions; associated with age (t = 1.9, p = 0.05) and hypertension (t = 2.9, p = 0.004), but not Apolipoprotein $\epsilon$4 status; and was greater in those with uncontrolled hypertension relative to controlled (t = 3.0, p = 0.003) and normotensive (t = 4.0, p = 0.0001) groups, suggesting that abnormal white matter may reflect currently active cerebrovascular effects. Abnormal white matter was highly heritable (a2 = 0.81) and shared some genetic influences with systolic blood pressure (rA = 0.26), although there was evidence for distinct genetic contributions and unique environmental influences. Future longitudinal research will shed light on factors impacting white matter disease presentation, progression, and potential recovery. },
}

@article{Dolz2016,
  year = { 2016 },
  volume = { 43 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients },
  pages = { 2569--2580 },
  number = { 5 },
  keywords = { autocontouring,lung cancer,organs at risk segmentation,radiotherapy,thoracic oncology,treatment planning },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4947484 },
  author = { Dolz and Kirişli and Fechter and Karnitzki and Oehlke and Nestle and Vermandel and Massoptier },
  abstract = { Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90{\%} of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50{\%} to 80{\%} in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP. },
}

@article{Cresson2016,
  year = { 2016 },
  volume = { 13 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988653888{\&}doi=10.1109{\%}2FLGRS.2016.2605138{\&}partnerID=40{\&}md5=c94a89e4590d599fb89db287d43a4c88 },
  type = { Journal Article },
  title = { A generic framework for the development of geospatial processing pipelines on clusters },
  pages = { 1706--1710 },
  number = { 11 },
  keywords = { Clusters,Message Passing Interface (MPI),Orfeo ToolBox (OTB),high-performance computing (HPC),parallel computing,remote sensing (RS) image processing },
  journal = { IEEE Geoscience and Remote Sensing Letters },
  issn = { 1545598X },
  eprint = { 1609.08893 },
  doi = { 10.1109/LGRS.2016.2605138 },
  author = { Cresson and Hautreux },
  arxivid = { 1609.08893 },
  archiveprefix = { arXiv },
  abstract = { The amount of remote sensing (RS) data available for applications is constantly growing due to the rise of very high resolution sensors and short-repeat-cycle satellites. Consequently, tackling the computational complexity in Earth observation information extraction is rising as a major challenge. Resorting to high-performance computing (HPC) is becoming a common practice, since this provides environments and programming facilities that are able to speed up processes. In particular, clusters are flexible cost-effective systems that are able to perform data-intensive tasks ideally fulfilling any computational requirement. However, their use typically implies a significant coding effort to build proper implementations of specific processing pipelines. This letter presents a generic framework for the development of RS images processing applications targeting cluster computing. It is based on common open-source libraries and leverages the parallelization of a wide variety of image processing pipelines in a transparent way. Performances on typical RS tasks implemented using the proposed framework demonstrate a great potential for the effective and timely processing of large amount of data. },
}

@article{Blackledge2016,
  year = { 2016 },
  volume = { 69 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX },
  pages = { 203--212 },
  keywords = { Computed tomography,Dicom management,Dicom visualisation,Medical imaging,OsiriX,Python,Radiology },
  journal = { Computers in Biology and Medicine },
  issn = { 18790534 },
  doi = { 10.1016/j.compbiomed.2015.12.002 },
  author = { Blackledge and Collins and Koh and Leach },
  abstract = { We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility.For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for all lesions, in agreement with a reduction in concurrent measures of serum prostate-specific antigen (PSA). },
}

@article{Beare2016,
  year = { 2016 },
  volume = { 10 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Neonatal brain tissue classification with morphological adaptation and unified segmentation },
  pages = { 17 },
  number = { MAR },
  keywords = { Magnetic resonance imaging,Neonate,Preterm birth,Statistical parametric mapping,Tissue classification },
  journal = { Frontiers in Neuroinformatics },
  issn = { 16625196 },
  doi = { 10.3389/fninf.2016.00012 },
  author = { Beare and Chen and Kelly and Alexopoulos and Smyser and Rogers and Loh and Matthews and Cheong and Spittle and Anderson and Doyle and Inder and Seal and Thompson },
  abstract = { Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born {\textless}30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS' performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis },
}

@article{Aksenov2016a,
  year = { 2016 },
  volume = { 167 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Eyeblink classical conditioning and BOLD fMRI of anesthesia-induced changes in the developing brain },
  pages = { 10--15 },
  keywords = { Anesthesia,Infant,Learning },
  journal = { Physiology and Behavior },
  issn = { 1873507X },
  doi = { 10.1016/j.physbeh.2016.08.030 },
  author = { Aksenov and Miller and Li and Wyrwicz },
  abstract = { Millions of children undergo general anesthesia each year in the USA alone, and a growing body of literature from animals and humans suggests that exposure to anesthesia at an early age can impact neuronal development, leading to learning and memory impairments later in childhood. Although a number of studies have reported behavioral and structural effects of anesthesia exposure during infancy, the functional manifestation of these changes has not been previous examined. In this study we used BOLD fMRI to measure the functional response to stimulation in the whisker barrel cortex of awake rabbits before and after learning a trace eyeblink classical conditioning paradigm. The functional changes, in terms of activated volume and time course, in rabbits exposed to isoflurane anesthesia during infancy was compared to unanesthetized controls when both groups reached young adulthood. Our findings show that whereas both groups exhibited decreased BOLD response duration after learning, the anesthesia-exposed group also showed a decrease in BOLD response volume in the whisker barrel cortex, particularly in the deeper infragranular layer. These results suggest that anesthesia exposure during infancy may affect the intracortical processes that mediate learning-related plasticity. },
}

@article{Achterberg2016,
  year = { 2016 },
  volume = { 3 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048206888{\&}doi=10.3389{\%}2Ffict.2016.00015{\&}partnerID=40{\&}md5=453da670107b4919c49586ea9b393a99 },
  type = { Journal Article },
  title = { Fastr: A Workflow engine for advanced data flows in medical image analysis },
  number = { AUG },
  keywords = { Data flow,Data processing,Distributed computing,Pipeline,Provenance,Python,Reproducible research,Workflow },
  journal = { Frontiers in ICT },
  issn = { 2297198X },
  doi = { 10.3389/fict.2016.00015 },
  author = { Achterberg and Koek and Niessen },
  abstract = { With the increasing number of datasets encountered in imaging studies, the increasing complexity of processing workflows, and a growing awareness for data stewardship, there is a need for managed, automated workflows. In this paper, we introduce Fastr, an automated workflow engine with support for advanced data flows. Fastr has built-in data provenance for recording processing trails and ensuring reproducible results. The extensible plugin-based design allows the system to interface with virtually any image archive and processing infrastructure. This workflow engine is designed to consolidate quantitative imaging biomarker pipelines in order to enable easy application to new data. },
}

@article{RN913,
  year = { 2016 },
  volume = { 264 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978744015{\&}doi=10.1111{\%}2Fjmi.12440{\&}partnerID=40{\&}md5=eaacef5f6889af31fbaf82da8a2c4344 },
  type = { Journal Article },
  title = { Characterizing microcrack orientation distribution functions in osteonal bone samples },
  pages = { 268--281 },
  number = { 3 },
  keywords = { Cortical bone,X-ray phase micro-tomography,microcrack segmentation,microdamage,orientation distribution function,synchrotron radiation },
  journal = { Journal of Microscopy },
  issn = { 13652818 },
  doi = { 10.1111/jmi.12440 },
  author = { Wolfram and Schwiedrzik and Mirzaali and B{\"{U}}RKI and Varga and Olivier and Peyrin and Zysset },
  abstract = { Prefailure microdamage in bone tissue is considered to be the most detrimental factor in defining its strength and toughness with respect to age and disease. To understand the influence of microcracks on bone mechanics it is necessary to assess their morphology and three-dimensional distribution. This requirement reaches beyond classic histology and stereology, and methods to obtain such information are currently missing. Therefore, the aim of the study was to develop a methodology that allows to characterize three-dimensional microcrack distributions in bulk bone samples. Four dumbbell-shaped specimens of human cortical bone of a 77-year-old female donor were loaded beyond yield in either tension, compression or torsion (one control). Subsequently, synchrotron radiation micro-computed tomography (SR$\mu$CT) was used to obtain phase-contrast images of the damaged samples. A microcrack segmentation algorithm was developed and used to segment microcrack families for which microcrack orientation distribution functions were determined. Distinct microcrack families were observed for each load case that resulted in distinct orientation distribution functions. Microcracks had median areas of approximately 4.7 $\mu$m2, 33.3 $\mu$m2 and 64.0 $\mu$m2 for tension, compression and torsion. Verifying the segmentation algorithm against a manually segmented ground truth showed good results when comparing the microcrack orientation distribution functions. A size dependence was noted when investigating the orientation distribution functions with respect to the size of the volume of interest used for their determination. Furthermore, a scale separation between tensile, compressive and torsional microcracks was noticeable. Visual comparison to classic histology indicated that microcrack families were successfully distinguished. We propose a methodology to analyse three-dimensional microcrack distributions in overloaded cortical bone. Such information could improve our understanding of bone microdamage and its impact on bone failure in relation to tissue age and disease. },
}

@article{RN933,
  year = { 2016 },
  volume = { 11 },
  type = { Journal Article },
  title = { Multimodal image fusion with SIMS: Preprocessing with image registration },
  pages = { 02A311 },
  number = { 2 },
  journal = { Biointerphases },
  issn = { 1934-8630 },
  doi = { 10.1116/1.4939892 },
  author = { Tarolli and Bloom and Winograd },
  abstract = { {\textcopyright} 2016 American Vacuum Society. In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important. To ensure that two input images are of the same region, an implementation of the insight segmentation and registration toolkit (ITK) was developed to act as a preprocessing step before performing image fusion. This implementation of ITK allows for several degrees of movement between two input images to be accounted for, including translation, rotation, and scale transforms. First, the implementation was confirmed to accurately register two multimodal images by supplying a known transform. Once validated, two model systems, a copper mesh grid and a group of RAW 264.7 cells, were used to demonstrate the use of the ITK implementation to register a SIMS image with a microscopy image for the purpose of performing image fusion. },
}

@article{RN934,
  year = { 2016 },
  volume = { 92 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986000629{\&}doi=10.15199{\%}2F48.2016.09.61{\&}partnerID=40{\&}md5=311e10c8007ae785c0cef57929b23170 },
  type = { Journal Article },
  title = { Algorytm doboru optymalnych parametr{\'{o}}w analizy skupie{\'{n}} zastosowanej do redukcji nadsegmentacji },
  pages = { 250--256 },
  number = { 9 },
  keywords = { Cluster analysis,Over-segmentation reduction,Watershed transformation },
  journal = { Przeglad Elektrotechniczny },
  issn = { 00332097 },
  doi = { 10.15199/48.2016.09.61 },
  author = { Smo{\l}ka and Skublewska-Paszkowska and {\L}ukasik },
  abstract = { The authors proposed a solution to the over-segmentation of color images processed by watershed segmentation algorithm. The solution utilizes hierarchical cluster analysis and treats watersheds as objects characterized by a number of attributes. This paper briefly discusses the solution (clustering methods, their parameters, selected watershed attributes) and presents an algorithm used for selecting optimal parameters for cluster analysis. Detailed results obtained for a set of test images are presented and discussed. },
}

@article{RN917,
  year = { 2016 },
  volume = { 32 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992718263{\&}doi=10.1016{\%}2Fj.ejmp.2016.09.009{\&}partnerID=40{\&}md5=3de38bf62efd4bb22fcce5398852ce95 },
  type = { Journal Article },
  title = { Clinical implementation of a Monte Carlo based treatment plan QA platform for validation of Cyberknife and Tomotherapy treatments },
  pages = { 1225--1237 },
  number = { 10 },
  keywords = { Delivered dose,Monte Carlo,QA,Treatment planning },
  journal = { Physica Medica },
  issn = { 1724191X },
  doi = { 10.1016/j.ejmp.2016.09.009 },
  author = { Reynaert and Demol and Charoy and Bouchoucha and Crop and Wagner and Lacornerie and Dubus and Rault and Comte and Cayez and Boydev and Pasquier and Mirabel and Lartigau and Sarrazin },
  abstract = { Purpose The main focus of the current paper is the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, without adding additional tasks to the dosimetry department. Methods The Monte Carlo platform consists of C++ classes for the actual functionality and a web based GUI that allows accessing the system using a web browser. Calculations are based on BEAMnrc/DOSXYZnrc and/or GATE and are performed automatically after exporting the dicom data from the treatment planning system. For Cyberknife treatments of moving targets, the log files saved during the treatment (position of robot, internal fiducials and external markers) can be used in combination with the 4D planning CT to reconstruct the actually delivered dose. The Monte Carlo platform is also used for calculation on MRI images, using pseudo-CT conversion. Results For Tomotherapy treatments we obtain an excellent agreement (within 2{\%}) for almost all cases. However, we have been able to detect a problem regarding the CT Hounsfield units definition of the Toshiba Large Bore CT when using a large reconstruction diameter. For Cyberknife treatments we obtain an excellent agreement with the Monte Carlo algorithm of the treatment planning system. For some extreme cases, when treating small lung lesions in low density lung tissue, small differences are obtained due to the different cut-off energy of the secondary electrons. Conclusions A Monte Carlo based treatment plan validation tool has successfully been implemented in clinical routine and is used to systematically validate all Cyberknife and Tomotherapy plans. },
}

@article{RN828,
  year = { 2016 },
  volume = { 14 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Possum—A Framework for Three-Dimensional Reconstruction of Brain Images from Serial Sections },
  pages = { 265--278 },
  number = { 3 },
  keywords = { 3D reconstruction,Brain atlas,Histology,Image analysis,Image registration,Light microscopy },
  journal = { Neuroinformatics },
  issn = { 15392791 },
  doi = { 10.1007/s12021-015-9286-1 },
  author = { Majka and W{\'{o}}jcik },
  abstract = { Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one's specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories. },
}

@article{RN931,
  year = { 2016 },
  volume = { 2016 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008957755{\&}doi=10.1155{\%}2F2016{\%}2F7419307{\&}partnerID=40{\&}md5=94c05725af9454145efeff48e39a8b1e },
  type = { Journal Article },
  title = { A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images },
  journal = { Computational and Mathematical Methods in Medicine },
  issn = { 17486718 },
  doi = { 10.1155/2016/7419307 },
  author = { Du and Dang and Wang and Wang and Lei },
  abstract = { The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU). },
}

@article{RN916,
  year = { 2016 },
  volume = { 43 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988808490{\&}doi=10.1118{\%}2F1.4963213{\&}partnerID=40{\&}md5=d555238a514a92d5a318ee19a0427dba },
  type = { Journal Article },
  title = { Individually optimized contrast-enhanced 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinoma },
  pages = { 5659--5666 },
  number = { 10 },
  keywords = { 4D-CT,contrast enhancement,pancreatic ductal adenocarcinoma,radiotherapy simulation },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4963213 },
  author = { Choi and Xue and Lane and Kang and Patel and Regine and Klahr and Wang and Chen and D'Souza and Lu },
  abstract = { Purpose: To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). Methods: Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. Image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) were compared in the three CTs. Interobserver variations were also evaluated in contouring the tumor using simultaneous truth and performance level estimation. Results: Average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P = 0.082), and both were significantly better than that for 4D-CT (2.6, P {\textless} 0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 Hounsfield units (HU), respectively; P = 0.21), and the latter was significantly higher than in 4D-CT (9.2 HU, P = 0.001). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P = 0.013) and 4D-CT (19.4 HU, P = 0.009). CNRs were comparable in CE 3D-CT and CE 4D-CT (1.4 and 0.8, respectively; P = 0.42), and both were significantly better in 4D-CT (0.6, P = 0.008 and 0.014). Mean tumor volumes were significantly smaller in CE 3D-CT (29.8 cm3, P = 0.03) and CE 4D-CT (22.8 cm3, P = 0.01) than in 4D-CT (42.0 cm3). Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P = 0.17). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0{\%} and 61.9{\%}, respectively) and were worse for 4D-CT (55.6{\%}) than CE 3D-CT. Conclusions: CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan. },
}

@article{RN971,
  year = { 2016 },
  volume = { 30 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964707329{\&}partnerID=40{\&}md5=77f7b0fdb11032a6d00501e3da414f03 },
  type = { Journal Article },
  title = { Intrinsic registration techniques for medical images: A state-of-the-art review },
  pages = { 119--132 },
  number = { 2 },
  keywords = { Image guided surgery,Intrinsic registration,Medical image registration,Medical imaging modalities },
  journal = { Journal of Postgraduate Medical Institute },
  issn = { 18119387 },
  author = { Alam and Rahman },
  abstract = { Medical image registration is the process of mapping two or more medical images into a single more informative image for the purpose of receiving precise and complementary information. The precise mapping of medical images obtained in different time-frames and by the same or different modalities is now possible due to the availability of large number of registration techniques. The purpose of this paper is to present and analyse intrinsic registration techniques for medical imaging in a comprehensive manner. Our approach of analysis is unique from already published work because we have performed detailed investigation on each registration techniques, and analyse similarity measures and assessments according to various parameters. The knowledge on the work that has been developed in the area is presented in a compact form. This work is expected to provide a useful platform for the researchers in the field of medical image registration in general and in intrinsic registration in particular. },
}

@incollection{RN930,
  year = { 2016 },
  volume = { 585 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960396227{\&}doi=10.1007{\%}2F978-3-319-30222-5{\_}23{\&}partnerID=40{\&}md5=00b082a6118fc326c7a012f9772959cf },
  type = { Serial },
  title = { Vessel segmentation for noisy CT data with quality measure based on single-point contrast-to-noise ratio },
  publisher = { Springer Verlag },
  pages = { 490--507 },
  keywords = { CUDA,Contrast to noise ratio,Fast marching,GPGPU,Geodesic active contours,Liver,Proximal algorithms,Total variance de-noising,Vessels segmentation,Xeon phi },
  issn = { 18650929 },
  isbn = { 9783319302218 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-3-319-30222-5_23 },
  booktitle = { Communications in Computer and Information Science },
  author = { Nikonorov and Kolsanov and Petrov and Yuzifovich and Prilepin and Chaplygin and Zelter and Bychenkov },
  abstract = { This paper describes a comprehensive multi-step algorithm for vascular structure segmentation in CT scan data, from raw slice images to a 3D object, with an emphasis on improving segmentation quality and assessing computational complexity. To estimate initial image quality and to evaluate denoising in the absence of the noise-free image, we propose a semi-global contrast-to-noise quality metric. We show that total variation-based filtering in the L1 metric results in the best denoising when compared to widely used nonlocal means or anisotropic diffusion denoising. To address higher computational complexity of our denoising algorithm, we created two high performance implementations, using Intel MIC and NVIDIA CUDA and compared results. In combination with proposed nearly real-time incremental segmentation technique, it provides fast and framework with controlled quality. },
}

@inproceedings{Miri2016,
  year = { 2016 },
  volume = { 9788 },
  url = { http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2214209 },
  title = { Comparative study of multimodal intra-subject image registration methods on a publicly available database },
  pages = { 97881Z },
  month = { mar },
  keywords = { Iterative closest point,Multimo,[Intensity-based,intensity-based,iterative closest point,multimodal registration,mutual information,point-based },
  issn = { 16057422 },
  isbn = { 9781510600232 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Miri et al/Medical Imaging 2016 Biomedical Applications in Molecular, Structural, and Functional Imaging/Miri et al. - 2016 - Comparative study of multimodal intra-subject image registration methods on a publicly available database.pdf:pdf },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2214209 },
  booktitle = { Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging },
  author = { Miri and Ghayoor and Johnson and Sonka },
  annote = { From Duplicate 2 (Comparative study of multimodal intra-subject image registration methods on a publicly available database - Miri, M.S.; Ghayoor, Ali; Johnson, Hans J.; Sonka, M.)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#} },
  abstract = { {\textcopyright} 2016 SPIE.This work reports on a comparative study between five manual and automated methods for intra-subject pair-wise registration of images from different modalities. The study includes a variety of inter-modal image registrations (MR-CT, PET-CT, PET-MR) utilizing different methods including two manual point-based techniques using rigid and similarity transformations, one automated point-based approach based on Iterative Closest Point (ICP) algorithm, and two automated intensity-based methods using mutual information (MI) and normalized mutual information (NMI). These techniques were employed for inter-modal registration of brain images of 9 subjects from a publicly available dataset, and the results were evaluated qualitatively via checkerboard images and quantitatively using root mean square error and MI criteria. In addition, for each inter-modal registration, a paired t-test was performed on the quantitative results in order to find any significant difference between the results of the studied registration techniques. },
}

@inproceedings{Gerard2016,
  year = { 2016 },
  title = { Alpha Shapes for Lung Segmentation in the Presence of Large Tumors },
  pages = { 1--9 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Gerard et al/PIA Pulmonary Image Analysis/Gerard et al. - 2016 - Alpha Shapes for Lung Segmentation in the Presence of Large Tumors.pdf:pdf },
  booktitle = { PIA: Pulmonary Image Analysis },
  author = { Gerard and Johnson and Bayouth and Christensen and Du and Guo and Reinhardt },
  annote = { \#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\# },
  abstract = { Lung segmentation is a critical initial step in planning radiation therapy interventions for lung cancer patients. Achieving robust automatic segmentation of lungs with large tumors is challenging due to large variations in lung morphology, tumor location, and tumor shape between subjects. We present an automatic method to segment lungs with large tumors in CT images using an initial intensity based segmentation followed by alpha shape construction and graph search. We evaluated our method by comparing automated segmentations to manual segmentations on twelve subjects. Computed metrics for segmentation quality include average surface distance of 0.727 mm and average DICE coefficient of 0.970. These results demonstrate that the proposed method accurately segments the entire lung regions both free of and in the presence of large tumors. },
}

@inproceedings{ghayoor2016tissue,
  year = { 2016 },
  volume = { 9784 },
  url = { http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2216625 },
  title = { Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method },
  pages = { 97841V },
  organization = { International Society for Optics and Photonics },
  month = { mar },
  keywords = { expectation maximization,fuzzy k-nearest neighborhood method,multi-site studies,neurodegenerative diseases,segmentation,tissue classification },
  issn = { 16057422 },
  isbn = { 9781510600195 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Ghayoor et al/Medical Imaging 2016 Image Processing/Ghayoor et al. - 2016 - Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method.pdf:pdf },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2216625 },
  booktitle = { Medical Imaging 2016: Image Processing },
  author = { Ghayoor and Paulsen and Kim and Johnson },
  annote = { From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#} },
  abstract = { This paper describes enhancements to automate classification of brain tissues formulti-site degenerative magnetic resonance imaging (MRI) data analysis. Processing of large collections of MR images is a key research technique to advance our understanding of the human brain. Previous studies have developed a robust multi-modal tool for automated tissue classification of large-scale data based on expectation maximization (EM) method initialized by group-wise prior probability distributions. This work aims to augment the EM-based classification using a non-parametric fuzzy k-Nearest Neighbor (k-NN) classifier that can model the unique anatomical states of each subject in the study of degenerative diseases. The presented method is applicable to multi-center heterogeneous data analysis and is quantitatively validated on a set of 18 synthetic multi-modal MR datasets having six different levels of noise and three degrees of bias-field provided with known ground truth. Dice index and average Hausdorff distance are used to compare the accuracy and robustness of the proposed method to a state-of-the-art classification method implemented based on EM algorithm. Both evaluation measurements show that presented enhancements produce superior results as compared to the EM only classification. },
}

@inproceedings{Gerard2016,
  year = { 2016 },
  title = { Alpha Shapes for Lung Segmentation in the Presence of Large Tumors },
  pages = { 1--9 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Gerard et al/PIA Pulmonary Image Analysis/Gerard et al. - 2016 - Alpha Shapes for Lung Segmentation in the Presence of Large Tumors.pdf:pdf },
  booktitle = { PIA: Pulmonary Image Analysis },
  author = { Gerard and Johnson and Bayouth and Christensen and Du and Guo and Reinhardt },
  annote = { From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J; Bayouth, John E; Christensen, Gary E; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work. I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#} },
  abstract = { Lung segmentation is a critical initial step in planning radiation therapy interventions for lung cancer patients. Achieving robust automatic segmentation of lungs with large tumors is challenging due to large variations in lung morphology, tumor location, and tumor shape between subjects. We present an automatic method to segment lungs with large tumors in CT images using an initial intensity based segmentation followed by alpha shape construction and graph search. We evaluated our method by comparing automated segmentations to manual segmentations on twelve subjects. Computed metrics for segmentation quality include average surface distance of 0.727 mm and average DICE coefficient of 0.970. These results demonstrate that the proposed method accurately segments the entire lung regions both free of and in the presence of large tumors. },
}

@inproceedings{Chakraborty2016,
  year = { 2016 },
  volume = { 9790 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976497384{\&}doi=10.1117{\%}2F12.2216781{\&}partnerID=40{\&}md5=7e796a05f80c2deec426ba41b27b2284 },
  type = { Conference Proceedings },
  title = { Fast myocardial strain estimation from 3D ultrasound through elastic image registration with analytic regularization },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 979006 },
  issn = { 16057422 },
  isbn = { 9781510600256 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2216781 },
  booktitle = { Medical Imaging 2016: Ultrasonic Imaging and Tomography },
  author = { Chakraborty and Heyde and Alessandrini and D'hooge },
  address = { Bellingham },
  abstract = { {\textcopyright} 2016 SPIE. Image registration techniques using free-form deformation models have shown promising results for 3D myocardial strain estimation from ultrasound. However, the use of this technique has mostly been limited to research institutes due to the high computational demand, which is primarily due to the computational load of the regularization term ensuring spatially smooth cardiac strain estimates. Indeed, this term typically requires evaluating derivatives of the transformation field numerically in each voxel of the image during every iteration of the optimization process. In this paper, we replace this time-consuming step with a closed-form solution directly associated with the transformation field resulting in a speed up factor of ∼10-60,000, for a typical 3D B-mode image of 250 3  and 500 3  voxels, depending upon the size and the parametrization of the transformation field. The performance of the numeric and the analytic solutions was contrasted by computing tracking and strain accuracy on two realistic synthetic 3D cardiac ultrasound sequences, mimicking two ischemic motion patterns. Mean and standard deviation of the displacement errors over the cardiac cycle for the numeric and analytic solutions were 0.68±0.40 mm and 0.75±0.43 mm respectively. Correlations for the radial, longitudinal and circumferential strain components at end-systole were 0.89, 0.83 and 0.95 versus 0.90, 0.88 and 0.92 for the numeric and analytic regularization respectively. The analytic solution matched the performance of the numeric solution as no statistically significant differences (p {\textgreater} 0.05) were found when expressed in terms of bias or limits-of-Agreement. },
}

@inproceedings{Danilov,
  year = { 2016 },
  volume = { 1 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995513594{\&}doi=10.7712{\%}2F100016.1827.10770{\&}partnerID=40{\&}md5=3954727a98c6ce96faf2f9cd25fba8f7 },
  type = { Conference Proceedings },
  title = { Image segmentation techniques for biomedical modeling: Electrophysiology and hemodynamics },
  pages = { 454--461 },
  keywords = { Electrophysiology,Hemodynamics,Image segmentation,Medical images,Mesh generation },
  isbn = { 9786188284401 },
  doi = { 10.7712/100016.1827.10770 },
  booktitle = { ECCOMAS Congress 2016 - Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering },
  author = { Danilov and Pryamonosov and Yurova },
  abstract = { The work addresses segmentation techniques for generation of individualized computational domains on the basis of medical imaging dataset. The computational domains will be used in 3D electrophysiology models and 3D-1D coupled hemodynamics models. Several techniques for user-guided and automated segmentation of soft tissues, segmentation of vascular and tubular structures, generation of centerlines, 1D network reconstruction, correction and local adaptation are examined. We propose two algorithms for automatic vascular network segmentation and user-guided cardiac segmentation. },
}

@inproceedings{RN927,
  year = { 2016 },
  volume = { 1576 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978488492{\&}partnerID=40{\&}md5=ce7b9f7668691df1ee707e7ab33ab66e },
  type = { Conference Proceedings },
  title = { A parallel algorithm for 3D reconstruction of internal organs according to imaging based on the active contour model },
  publisher = { CEUR-WS },
  pages = { 482--489 },
  keywords = { 3D reconstruction,3D segmentation,Reference geometrical model,Segmentation,Tomography active contour method },
  issn = { 16130073 },
  isbn = { 16130073 (ISSN) },
  editor = { [object Object],[object Object] },
  booktitle = { CEUR Workshop Proceedings },
  author = { Vasil'ev and Belokamenskaja and Novozhilov and Turlapov },
  abstract = { In this paper we consider the problem of building parallel methods for three-dimensional human organs' geometrical reconstruction based on the active contour method. The idea of computing decomposition is based on opportunity to visit all layers in both directions from the starting layer and use several starting layers that have initial contours generated from the reference body geometrical model. Using ITK library we have implemented parallel kidney segmentation algorithms. The first parallel version gave acceleration of about 1.6 for 2 threads on CPU, and the second - a significant increase in efficiency with an increase in the number of points in the contour, but in the range 3.2 times for 8 threads on CPU. We are planning to transfer the algorithm on the GPU. },
}

@inproceedings{RN928,
  year = { 2016 },
  volume = { 9785 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988892588{\&}doi=10.1117{\%}2F12.2216303{\&}partnerID=40{\&}md5=eab17b988f48475720c13a6e6b359caa },
  type = { Conference Proceedings },
  title = { A B-spline image registration based CAD scheme to evaluate drug treatment response of ovarian cancer patients },
  publisher = { SPIE },
  pages = { 97853D },
  issn = { 16057422 },
  isbn = { 9781510600201 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2216303 },
  booktitle = { Medical Imaging 2016: Computer-Aided Diagnosis },
  author = { Tan and Li and Moore and Thai and Ding and Liu and Zheng },
}

@inproceedings{RN977,
  year = { 2016 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010216516{\&}doi=10.1109{\%}2FCVPRW.2016.78{\&}partnerID=40{\&}md5=0d777eb051999b3ff620a82e4ec05c67 },
  type = { Conference Proceedings },
  title = { SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration },
  publisher = { IEEE Computer Society },
  pages = { 574--582 },
  issn = { 21607516 },
  isbn = { 9781467388504 },
  doi = { 10.1109/CVPRW.2016.78 },
  booktitle = { IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops },
  author = { Marstal and Berendsen and Staring and Klein },
  abstract = { In this paper we present SimpleElastix, an extension of SimpleITK designed to bring the Elastix medical image registration library to a wider audience. Elastix is a modular collection of robust C++ image registration algorithms that is widely used in the literature. However, its command-line interface introduces overhead during prototyping, experimental setup, and tuning of registration algorithms. By integrating Elastix with SimpleITK, Elastix can be used as a native library in Python, Java, R, Octave, Ruby, Lua, Tcl and C{\#} on Linux, Mac and Windows. This allows Elastix to intregrate naturally with many development environments so the user can focus more on the registration problem and less on the underlying C++ implementation. As means of demonstration, we show how to register MR images of brains and natural pictures of faces using minimal amount of code. SimpleElastix is open source, licensed under the permissive Apache License Version 2.0 and available at https://github.com/kaspermarstal/SimpleElastix. },
}

@inproceedings{RN914,
  year = { 2016 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004072339{\&}doi=10.1109{\%}2FIST.2016.7738233{\&}partnerID=40{\&}md5=aa3e12d899e600e256992bc3fbaface3 },
  type = { Conference Proceedings },
  title = { Registration of different phases of contrast-enhanced CT for facilitation of partial nephrectomy },
  publisher = { Institute of Electrical and Electronics Engineers Inc. },
  pages = { 255--260 },
  keywords = { B-spline registration,image registration,medical image processing,partial nephrectomy,segmentation,surgery planning },
  isbn = { 9781509018178 },
  doi = { 10.1109/IST.2016.7738233 },
  booktitle = { IST 2016 - 2016 IEEE International Conference on Imaging Systems and Techniques, Proceedings },
  author = { Heryan and Skalski and Gajda and Drewniak and Jakubowski },
  abstract = { From the preoperative partial nephrectomy planning perspective, it is essential to expose separately different kidney structures and to analyze their mutual topological relations. Only then, the identification of possible conflicts prior to surgical intervention can be facilitated. To enable this, we propose a segmentation frameworks for renal vascular tree, kidney and pelvicalyceal system from corresponding CT phases. In order to compensate for both patient position changes and volumetric changes related to respiratory activity, registration of different CT phases is required. It is performed by combining global rigid transform with multilevel and multiresolution B-spline registration. The research material consisted of fifteen patients that underwent CT scanning preceding kidney cancer surgery. Presented results using checkerboards and differential images prove the effectiveness of the proposed method. In addition, visualizations of the segmented structures (renal arteries, kidney, pelvicalyceal system) from registered CT phases are provided to exemplary demonstrate individual model for preoperative planning. This kind of solution meets the expectations of urological oncology in terms of facilitating planning the optimal surgical approach in partial nephrectomy. To the best of our knowledge, such a comprehensive strategy involving both the proposed segmentation frameworks and registration has not been introduced yet. },
}

@inproceedings{RN926,
  year = { 2016 },
  volume = { 9784 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981736248{\&}doi=10.1117{\%}2F12.2216537{\&}partnerID=40{\&}md5=0c6892196eefd59bd4552f6498d124be },
  type = { Conference Proceedings },
  title = { Pancreas and cyst segmentation },
  publisher = { SPIE },
  pages = { 97842C },
  issn = { 16057422 },
  isbn = { 9781510600195 },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.1117/12.2216537 },
  booktitle = { Medical Imaging 2016: Image Processing },
  author = { Dmitriev and Gutenko and Nadeem and Kaufman },
}

@inproceedings{RN912,
  year = { 2016 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010223955{\&}doi=10.1109{\%}2FCVPRW.2016.69{\&}partnerID=40{\&}md5=dc7baf79ffe3f2bf7316f023fd6aa7a1 },
  type = { Conference Proceedings },
  title = { The Design of SuperElastix - A Unifying Framework for a Wide Range of Image Registration Methodologies },
  publisher = { IEEE Computer Society },
  pages = { 498--506 },
  issn = { 21607516 },
  isbn = { 9781467388504 },
  doi = { 10.1109/CVPRW.2016.69 },
  booktitle = { IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops },
  author = { Berendsen and Marstal and Klein and Staring },
  abstract = { A large diversity of image registration methodologies has emerged from the research community. The scattering of methods over toolboxes impedes rigorous comparison to select the appropriate method for a given application. Toolboxes typically tailor their implementations to a mathematical registration paradigm, which makes internal functionality nonexchangeable. Subsequently, this forms a barrier for adoption of registration technology in the clinic. We therefore propose a unifying, role-based software design that can integrate a broad range of functional registration components. These components can be configured into an algorithmic network via a single highlevel user interface. A generic component handshake mechanism provides users feedback on incompatibilities. We demonstrate the viability of our design by incorporating two paradigms from different code bases. The implementation is done in C++ and is available as open source. The progress of embedding more paradigms can be followed via https://github.com/kaspermarstal/SuperElastix. },
}

@book{Johnson2015a,
  year = { 2015 },
  url = { https://itk.org/ },
  title = { The ITK Software Guide Book 1: Introduction and Development Guidelines Fourth Edition Updated for ITK version 4.7 },
  isbn = { 978-1930934276 },
  booktitle = { Kitware, Inc.(January 2015) },
  author = { Johnson and McCormick and Ibanez },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired fromsuchmedical instru- mentation as CT orMRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in themedical environment, a CT scan may be aligned with aMRI scan in order to combine the information contained in both. },
}

@book{Yarbs2015,
  year = { 2015 },
  volume = { 86 },
  url = { http://doi.apa.org/getdoi.cfm?doi=10.1037/a0022758{\%}0Ahttp://dx.doi.org/10.1016/j.biopsych.2008.08.011 },
  title = { Third edition : revised and updated World },
  pmid = { 15312695 },
  pages = { 860--865 },
  number = { 1 },
  keywords = { Antisaccade,Attraction,Automatic processing,Bipolar depression,Chest,E-Z Reader,Emotion,Endophenotype,Eye movement,Eye movement control,Eye-tracking,Friendship,Keith Rayner,Mating,Negative emotionality,Neuroticism,Oculomotor control,Prosaccade (reflexive saccade,Saccade,Schizophrenia,Schizotypal personality traits,Sentences and discourses,Unipolar depression,Visual scenes,Waist-to-hip ratio,Word recognition,behavioral adaptation,dimberg,e,emotion,error positivity,error signaling,error-related negativity,eye-tracking,facial expressions,facial expressions contain information,fast and automatic processing,g,havior,http://www.archive.org/details/eyebrainpsycholo00r,information is optimized,psychopathy,relevant for social be-,scan path,the processing of facial,thunberg,thus,visually-guided saccade) },
  issn = { 1931-1516 },
  isbn = { 0749-596X },
  doi = { 10.1016/j.biopsycho.2004.03.014 },
  booktitle = { Problems of Information Transmission },
  author = { Yarbs and Haigh and Ettinger and Kumari and Crawford and Flak and Sharma and Davis and Corr and Brazil and Bruijn and Bulten and Borries and Lankveld and Buitelaar and Verkes and Eisenbarth and Alpers and Singler and Gillath and Bahns and Burghart and Clifton and Ferreira and Henderson and Inhoff and Liversedge and Reichle and Schotter and Carvalho and Laurent and Noiret and Chopard and Haffen and Bennabi and Vandel and Gregory },
  abstract = { BACKGROUND: The analysis of eye movements (EM) by eye-tracking has been carried out for several decades to investigate mood regulation, emotional information processing, and psychomotor disturbances in depressive disorders.$\backslash$nMETHOD: A systematic review of all English language PubMed articles using the terms "saccadic eye movements" OR "eye-tracking" AND "depression" OR "bipolar disorders" was conducted using PRISMA guidelines. The aim of this review was to characterize the specific alterations of EM in unipolar and bipolar depression.$\backslash$nRESULTS: Findings regarding psychomotor disturbance showed an increase in reaction time in prosaccade and antisaccade tasks in both unipolar and bipolar disorders. In both disorders, patients have been reported to have an attraction for negative emotions, especially for negative pictures in unipolar and threatening images in bipolar disorder. However, the pattern could change with aging, elderly unipolar patients disengaging key features of sad and neutral stimuli. METHODological limitations generally include small sample sizes with mixed unipolar and bipolar depressed patients.$\backslash$nCONCLUSION: Eye movement analysis can be used to discriminate patients with depressive disorders from controls, as well as patients with bipolar disorder from patients with unipolar depression. General knowledge concerning psychomotor alterations and affective regulation strategies associated with each disorder can also be gained thanks to the analysis. Future directions for research on eye movement and depression are proposed in this review. },
}

@book{johnson2014itk,
  year = { 2015 },
  url = { https://itk.org/ },
  title = { The ITK Software Guide Book 1: Introduction and Development Guidelines Fourth Edition Updated for ITK version 4.7 },
  isbn = { 978-1930934276 },
  editor = { [object Object],[object Object],[object Object] },
  edition = { 4.7 },
  booktitle = { Kitware, Inc.(January 2015) },
  author = { Johnson and McCormick and Ibanez },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired fromsuchmedical instru- mentation as CT orMRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in themedical environment, a CT scan may be aligned with aMRI scan in order to combine the information contained in both. },
}

@inbook{Meesters2015,
  year = { 2015 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { AUTOMATED IDENTIFICATION OF INTRACRANIAL DEPTH ELECTRODES IN COMPUTED TOMOGRAPHY DATA },
  series = { IEEE International Symposium on Biomedical Imaging },
  publisher = { Ieee },
  pages = { 976--979 },
  isbn = { 978-1-4799-2374-8 },
  booktitle = { 2015 IEEE 12th International Symposium on Biomedical Imaging },
  author = { Meesters and Ossenblok and Colon and Schijns and Florack and Boon and Wagner and Fuster and Ieee },
  address = { New York },
}

@inbook{Polfliet2015,
  year = { 2015 },
  volume = { 45 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Estimation of hip prosthesis migration: A study of zero migration },
  series = { IFMBE Proceedings },
  publisher = { Springer-Verlag Berlin },
  pages = { 126--129 },
  keywords = { Hip Prosthesis,Migration,Rigid Registration },
  issn = { 16800737 },
  isbn = { 9783319111278 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-3-319-11128-5_32 },
  booktitle = { IFMBE Proceedings },
  author = { Polfliet and Vandemeulebroucke and {Van Gompel} and Buls and Deklerck and Scheerlinck },
  address = { Berlin },
  abstract = { We present a method for automatically estimating prosthesis migration from previous and follow-up CT image data. The method consists of the segmentation of the bone and prosthesis in both images, followed by a registration of both substructures. The migration is found by computing the difference between both transforms. In this work we assess the accuracy of the method for zero migration. The method was applied on data from a mechanical phantom and on patient data, both with zero migration. Our experiments show that an accuracy of less than 0.3 mm can be achieved in a clinical setting. },
}

@article{johnson2015itk,
  year = { 2015 },
  title = { The ITK Software Guide Book 1: Introduction and Development Guidelines-Volume 1 },
  publisher = { Kitware, Inc. },
  keywords = { Guide,Registration,Segmentation },
  isbn = { 978-1-930934-28-3 },
  author = { Johnson and Mccormick and Ibanez },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registra tion and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is a n image acquired from such medical instru- mentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT s can may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environmen t known as CMake to manage platform- specific project generation and compilation process in a pla tform-independent way. ITK is imple- mented in C++. ITK's implementation style employs generic p rogramming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template . The use of C++ templating means that the code is highly efficient and many issues are discovered at compile-time, rather than at run-time during program execution. It also means that many of ITK's al gorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high- level programming language Python . This enables rapid prototyping and faster exploration of i deas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is avai lable for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, a nd extend the software because ITK is an open-source project. ITK uses a model of software devel opment known as Extreme Program- ming. Extreme Programming collapses the usual software dev elopment methodology into a simulta- neous iterative process of design-implement-test-releas e. The key features of Extreme Programming are communication and testing. Communication among the mem bers of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated co ntinuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf . This book is a guide to developing software with ITK; it is the first of two companion books. This book cove rs building and installation, general architecture and design, as well as the process of contribut ing in the ITK community. The second book covers detailed design and functionality for reading a nd writing images, filtering, registration, segmentation, and performing statistical analysis. },
}

@article{johnson2015itk,
  year = { 2015 },
  url = { https://itk.org/ },
  title = { The ITK Software Guide Book 1: Introduction and Development Guidelines Fourth Edition Updated for ITK version 4.7 },
  publisher = { Kitware, Inc. },
  keywords = { Guide,Registration,Segmentation },
  journal = { Kitware, Inc.(January 2015) },
  isbn = { 978-1930934276 },
  author = { Johnson and McCormick and Ibanez },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired fromsuchmedical instru- mentation as CT orMRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in themedical environment, a CT scan may be aligned with aMRI scan in order to combine the information contained in both. },
}

@article{Gaddy2015,
  year = { 2015 },
  volume = { 5 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928392739{\&}doi=10.1186{\%}2Fs13550-015-0096-0{\&}partnerID=40{\&}md5=9be19ccea7a7aa4683c93949d1fae115 },
  type = { Journal Article },
  title = { Whole-body organ-level and kidney micro-dosimetric evaluations of 64Cu-loaded HER2/ErbB2-targeted liposomal doxorubicin (64Cu-MM-302) in rodents and primates },
  pages = { 10 },
  number = { 1 },
  keywords = { Copper-64,Dosimetry,Nanotherapeutics,Positron emission tomography },
  journal = { EJNMMI Research },
  issn = { 2191219X },
  doi = { 10.1186/s13550-015-0096-0 },
  author = { Gaddy and Lee and Zheng and Jaffray and Wickham and Hendriks },
  abstract = { Background: Features of the tumor microenvironment influence the efficacy of cancer nanotherapeutics. The ability to directly radiolabel nanotherapeutics offers a valuable translational tool to obtain biodistribution and tumor deposition data, testing the hypothesis that the extent of delivery predicts therapeutic outcome. In support of a first in-human clinical trial with 64Cu-labeled HER2-targeted liposomal doxorubicin (64Cu-MM-302), a preclinical dosimetric analysis was performed. Methods: Whole-body biodistribution and pharmacokinetic data were obtained in mice that received 64Cu-MM-302 and used to estimate absorbed radiation doses in normal human organs. PET/CT imaging revealed non-uniform distribution of 64Cu signal in mouse kidneys. Kidney micro-dosimetry analysis was performed in mice and squirrel monkeys, using a physiologically based pharmacokinetic model to estimate the full dynamics of the 64Cu signal in monkeys. Results: Organ-level dosimetric analysis of mice receiving 64Cu-MM-302 indicated that the heart was the organ receiving the highest radiation absorbed dose, due to extended liposomal circulation. However, PET/CT imaging indicated that 64Cu-MM-302 administration resulted in heterogeneous exposure in the kidney, with a focus of 64Cu activity in the renal pelvis. This result was reproduced in primates. Kidney micro-dosimetry analysis illustrated that the renal pelvis was the maximum exposed tissue in mice and squirrel monkeys, due to the highly concentrated signal within the small renal pelvis surface area. Conclusions: This study was used to select a starting clinical radiation dose of 64Cu-MM-302 for PET/CT in patients with advanced HER2-positive breast cancer. Organ-level dosimetry and kidney micro-dosimetry results predicted that a radiation dose of 400 MBq of 64Cu-MM-302 should be acceptable in patients. },
}

@article{Yang2015,
  year = { 2015 },
  volume = { 42 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion },
  pages = { 4484--4496 },
  number = { 8 },
  keywords = { DIR,FEM,image fusion,lung cancer,radiotherapy },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4923167 },
  author = { Yang and Teo and {Van Reeth} and Tan and Tham and Poh },
  abstract = { Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the author's proposed approach.Methods: A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error {\textless}2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40{\%} error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles. },
}

@article{Xu2015,
  year = { 2015 },
  volume = { 10 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multi-slice-to-volume registration for MRI-guided transperineal prostate biopsy },
  pages = { 563--572 },
  number = { 5 },
  keywords = { Image registration,MRI-guidance,Prostate biopsy,Target localization },
  journal = { International Journal of Computer Assisted Radiology and Surgery },
  issn = { 18616429 },
  doi = { 10.1007/s11548-014-1108-7 },
  author = { Xu and Lasso and Fedorov and Tuncali and Tempany and Fichtinger },
  abstract = { Purpose: Prostate needle biopsy is a commonly performed procedure since it is the most definitive form of cancer diagnosis. Magnetic resonance imaging (MRI) allows target-specific biopsies to be performed. However, needle placements are often inaccurate due to intra-operative prostate motion and the lack of motion compensation techniques. This paper detects and determines the extent of tissue displacement during an MRI-guided biopsy so that the needle insertion plan can be adjusted accordingly. Methods: A multi-slice-to-volume registration algorithm was developed to align the pre-operative planning image volume with three intra-operative orthogonal image slices of the prostate acquired immediately before needle insertion. The algorithm consists of an initial rigid transformation followed by a deformable step. Results: A total of 14 image sets from 10 patients were studied. Based on prostate contour alignment, the registrations were accurate to within 2 mm. Conclusion: This algorithm can be used to increase the needle targeting accuracy by alerting the clinician if the biopsy target has moved significantly prior to needle insertion. The proposed method demonstrated feasibility of intra-operative target localization and motion compensation for MRI-guided prostate biopsy. },
}

@article{Weis2015,
  year = { 2015 },
  volume = { 2 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer },
  pages = { 036001 },
  number = { 3 },
  journal = { Journal of Medical Imaging },
  issn = { 2329-4302 },
  doi = { 10.1117/1.jmi.2.3.036001 },
  author = { Weis and Flint and Sanchez and Yankeelov and Miga },
  abstract = { {\textcopyright} 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was {\textless}12{\%}. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13{\%} at the bulk level and 32{\%} at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies. },
}

@article{Sparks2015,
  year = { 2015 },
  volume = { 42 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multiattribute probabilistic prostate elastic registration (MAPPER): Application to fusion of ultrasound and magnetic resonance imaging },
  pages = { 1153--1163 },
  number = { 3 },
  keywords = { MRI-ultrasound fusion,image registration,image-guided biopsy,prostate cancer,prostate imaging },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4905104 },
  author = { Sparks and {Nicolas Bloch} and Feleppa and Barratt and Moses and Ponsky and Madabhushi },
  abstract = { Purpose: Transrectal ultrasound (TRUS)-guided needle biopsy is the current gold standard for prostate cancer diagnosis. However, up to 40{\%} of prostate cancer lesions appears isoechoic on TRUS. Hence, TRUS-guided biopsy has a high false negative rate for prostate cancer diagnosis. Magnetic resonance imaging (MRI) is better able to distinguish prostate cancer from benign tissue. However, MRI-guided biopsy requires special equipment and training and a longer procedure time. MRI-TRUS fusion, where MRI is acquired preoperatively and then aligned to TRUS, allows for advantages of both modalities to be leveraged during biopsy. MRI-TRUS-guided biopsy increases the yield of cancer positive biopsies. In this work, the authors present multiattribute probabilistic postate elastic registration (MAPPER) to align prostate MRI and TRUS imagery. Methods: MAPPER involves (1) segmenting the prostate on MRI, (2) calculating a multiattribute probabilistic map of prostate location on TRUS, and (3) maximizing overlap between the prostate segmentation on MRI and the multiattribute probabilistic map on TRUS, thereby driving registration of MRI onto TRUS. MAPPER represents a significant advancement over the current state-of-the-art as it requires no user interaction during the biopsy procedure by leveraging texture and spatial information to determine the prostate location on TRUS. Although MAPPER requires manual interaction to segment the prostate on MRI, this step is performed prior to biopsy and will not substantially increase biopsy procedure time. Results: MAPPER was evaluated on 13 patient studies from two independent datasetsDataset 1 has 6 studies acquired with a side-firing TRUS probe and a 1.5 T pelvic phased-array coil MRI; Dataset 2 has 7 studies acquired with a volumetric end-firing TRUS probe and a 3.0 T endorectal coil MRI. MAPPER has a root-mean-square error (RMSE) for expert selected fiducials of 3.36±1.10 mm for Dataset 1 and 3.14±0.75 mm for Dataset 2. State-of-the-art MRI-TRUS fusion methods report RMSE of 3.062.07 mm. Conclusions: MAPPER aligns MRI and TRUS imagery without manual intervention ensuring efficient, reproducible registration. MAPPER has a similar RMSE to state-of-the-art methods that require manual intervention. },
}

@article{Reckfort2015,
  year = { 2015 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI },
  pages = { 11 },
  number = { september },
  keywords = { Brain,Connectome,Fiber orientation,Multiscale approach,Polarized light imaging },
  journal = { Frontiers in Neuroanatomy },
  issn = { 16625129 },
  doi = { 10.3389/fnana.2015.00118 },
  author = { Reckfort and Wiese and Pietrzyk and Zilles and Amunts and Axer },
  abstract = { Structural connectivity of the brain can be conceptionalized as a multiscale organization. The present study is built on 3D-Polarized Light Imaging (3D-PLI), a neuroimaging technique targeting the reconstruction of nerve fiber orientations and therefore contributing to the analysis of brain connectivity. Spatial orientations of the fibers are derived from birefringence measurements of unstained histological sections that are interpreted by means of a voxel-based analysis. This implies that a single fiber orientation vector is obtained for each voxel, which reflects the net effect of all comprised fibers. We have utilized two polarimetric setups providing an object space resolution of 1.3 $\mu$m/px (microscopic setup) and 64 $\mu$m/px (macroscopic setup) to carry out 3D-PLI and retrieve fiber orientations of the same tissue samples, but at complementary voxel sizes (i.e., scales). The present study identifies the main sources which cause a discrepancy of the measured fiber orientations observed when measuring the same sample with the two polarimetric systems. As such sources the differing optical resolutions and diverging retardances of the implemented waveplates were identified. A methodology was implemented that enables the compensation of measured different systems' responses to the same birefringent sample. This opens up new ways to conduct multiscale analysis in brains by means of 3D-PLI and to provide a reliable basis for the transition between different scales of the nerve fiber architecture. },
}

@article{Podlesek2015,
  year = { 2015 },
  volume = { 10 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Improved visualization of intracranial vessels with intraoperative coregistration of rotational digital subtraction angiography and intraoperative 3D ultrasound },
  pages = { 16 },
  number = { 3 },
  journal = { PLoS ONE },
  issn = { 19326203 },
  doi = { 10.1371/journal.pone.0121345 },
  author = { Podlesek and Meyer and Morgenstern and Schackert and Kirsch },
  abstract = { Introduction: Ultrasound can visualize and update the vessel status in real time during cerebral vascular surgery. We studied the depiction of parent vessels and aneurysms with a high-resolution 3D intraoperative ultrasound imaging system during aneurysm clipping using rotational digital subtraction angiography as a reference. Methods: We analyzed 3D intraoperative ultrasound in 39 patients with cerebral aneurysms to visualize the aneurysm intraoperatively and the nearby vascular tree before and after clipping. Simultaneous coregistration of preoperative subtraction angiography data with 3D intraoperative ultrasound was performed to verify the anatomical assignment. Results: Intraoperative ultrasound detected 35 of 43 aneurysms (81{\%}) in 39 patients. Thirty-nine intraoperative ultrasound measurements were matched with rotational digital subtraction angiography and were successfully reconstructed during the procedure. In 7 patients, the aneurysm was partially visualized by 3D-ioUS or was not in field of view. Post-clipping intraoperative ultrasound was obtained in 26 and successfully reconstructed in 18 patients (69{\%}) despite clip related artefacts. The overlap between 3D-ioUS aneurysm volume and preoperative rDSA aneurysm volume resulted in a mean accuracy of 0.71 (Dice coefficient). Conclusions: Intraoperative coregistration of 3D intraoperative ultrasound data with preoperative rotational digital subtraction angiography is possible with high accuracy. It allows the immediate visualization of vessels beyond the microscopic field, as well as parallel assessment of blood velocity, aneurysm and vascular tree configuration. Although spatial resolution is lower than for standard angiography, the method provides an excellent vascular overview, advantageous interpretation of 3D-ioUS and immediate intraoperative feedback of the vascular status. A prerequisite for understanding vascular intraoperative ultrasound is image quality and a successful match with preoperative rotational digital subtraction angiography. },
}

@article{Mutsvangwa2015,
  year = { 2015 },
  volume = { 62 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { An Automated Statistical Shape Model Developmental Pipeline: Application to the Human Scapula and Humerus },
  pages = { 1098--1107 },
  number = { 4 },
  keywords = { humerus,scapula,statistical shape model },
  journal = { IEEE Transactions on Biomedical Engineering },
  issn = { 15582531 },
  doi = { 10.1109/TBME.2014.2368362 },
  author = { Mutsvangwa and Burdin and Schwartz and Roux },
  abstract = { This paper presents development of statistical shape models based on robust and rigid-groupwise registration followed by pointset nonrigid registration. The main advantages of the pipeline include automation in that the method does not rely on manual landmarks or a regionalization step; there is no bias in the choice of reference during the correspondence steps and the use of the probabilistic principal component analysis framework increases the domain of the shape variability. A comparison between the widely used expectation maximization-iterative closest point algorithm and a recently reported groupwise method on publicly available data (hippocampus) using the well-known criteria of generality, specificity, and compactness is also presented. The proposed method gives similar values but the curves of generality and specificity are superior to those of the other two methods. Finally, the method is applied to the human scapula, which is a known difficult structure, and the human humerus. },
}

@article{Menard2015,
  year = { 2015 },
  volume = { 274 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { MR-guided prostate biopsy for planning of focal salvage after radiation therapy },
  pages = { 181--191 },
  number = { 1 },
  journal = { Radiology },
  issn = { 15271315 },
  doi = { 10.1148/radiol.14122681 },
  author = { M{\'{e}}nard and Iupati and Publicover and Lee and Abed and O'Leary and Simeonov and Foltz and Milosevic and Catton and Morton and Bristow and Bayley and Atenafu and Evans and Jaffray and Chung and Brock and Haider },
  abstract = { Purpose: To determine if the integration of diagnostic magnetic resonance (MR) imaging and MR-guided biopsy would improve target delineation for focal salvage therapy in men with prostate cancer.
Materials and Between September 2008 and March 2011, 30 men with bio-Methods: chemical failure after radiation therapy for prostate cancer provided written informed consent and were enrolled in a prospective clinical trial approved by the institutional research ethics board. An integrated diagnostic MR imaging and interventional biopsy procedure was performed with a 1.5-T MR imager by using a prototype table and stereotactic transperineal template. Multiparametric MR imaging (T2-weighted, dynamic contrast material-enhanced, and diffusion-weighted sequences) was followed by targeted biopsy of suspicious regions and systematic sextant sampling. Biopsy needle locations were imaged and registered to diagnostic images. Two observers blinded to clinical data and the results of prior imaging studies delineated tumor boundaries. Area under the receiver operating characteristic curve (Az) was calculated based on generalized linear models by using biopsy as the reference standard to distinguish benign from malignant lesions.
Results: Twenty-eight patients were analyzed. Most patients (n = 22) had local recurrence, with 82{\%} (18 of 22) having unifocal disease. When multiparametric volumes from two observers were combined, it increased the apparent overall tumor volume by 30{\%}; however, volumes remained small (mean, 2.9 mL; range, 0.5-8.3 mL). Tumor target boundaries differed between T2-weighted, dynamic contrast-enhanced, and diffusion-weighted sequences (mean Dice coefficient, 0.13-0.35). Diagnostic accuracy in the identification of tumors improved with a multiparametric approach versus a strictly T2-weighted or dynamic contrast-enhanced approach through an improvement in sensitivity (observer 1, 0.65 vs 0.35 and 0.44, respectively; observer 2, 0.82 vs 0.64 and 0.53, respectively; P {\textless} .05) and improved further with a 5-mm expansion margin (Az = 0.85 vs 0.91 for observer 2). After excluding three patients with fewer than six informative biopsy cores and six patients with inadequately stained margins, MR-guided biopsy enabled more accurate delineation of the tumor target volume be means of exclusion of false-positive results in 26{\%} (five of 19 patients), false-negative results in 11{\%} (two of 19 patients) and by guiding extension of tumor boundaries in 16{\%} (three of 19 patients).
Conclusion: The integration of guided biopsy with diagnostic MR imaging is feasible and alters delineation of the tumor target boundary in a substantial proportion of patients considering focal salvage. },
}

@article{Magnain2015,
  year = { 2015 },
  volume = { 2 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Optical coherence tomography visualizes neurons in human entorhinal cortex },
  pages = { 015004 },
  number = { 1 },
  journal = { Neurophotonics },
  issn = { 2329-423X },
  doi = { 10.1117/1.nph.2.1.015004 },
  author = { Magnain and Augustinack and Konukoglu and Frosch and Sakad{\v{z}}ic and Varjabedian and Garcia and Wedeen and Boas and Fischl },
  abstract = { Abstract. The cytoarchitecture of the human brain is of great interest in diverse fields: neuroanatomy, neurology, neuroscience, and neuropathology. Traditional histology is a method that has been historically used to assess cell and fiber content in the ex vivo human brain. However, this technique suffers from significant distortions. We used a previously demonstrated optical coherence microscopy technique to image individual neurons in several square millimeters of en-face tissue blocks from layer II of the human entorhinal cortex, over 50 $\mu$min depth. The same slices were then sectioned and stained for Nissl substance. We registered the optical coherence tomog- raphy (OCT) images with the corresponding Nissl stained slices using a nonlinear transformation. The neurons were then segmented in both images and we quantified the overlap. We show that OCT images contain infor- mation about neurons that is comparable to what can be obtained from Nissl staining, and thus can be used to assess the cytoarchitecture of the ex vivo human brain with minimal distortion. With the future integration of a vibratome into the OCT imaging rig, this technique can be scaled up to obtain undistorted volumetric data of centimeter cube tissue blocks in the near term, and entire human hemispheres in the future. },
}

@article{Korez2015,
  year = { 2015 },
  volume = { 34 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation },
  pages = { 1649--1662 },
  number = { 8 },
  keywords = { Computed tomography,deformable models,image segmentation,interpolation theory,object detection,spine,vertebra },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 1558254X },
  doi = { 10.1109/TMI.2015.2389334 },
  author = { Korez and Ibragimov and Likar and Pernus and Vrtovec },
  abstract = { Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6{\%} for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6{\%} for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images. },
}

@article{Jones2015,
  year = { 2015 },
  volume = { 246 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images },
  pages = { 13--21 },
  keywords = { 3D segmentation,Connectomics,Electron microscopy,Image segmentation,Neuron reconstruction,Semi-automatic segmentation },
  journal = { Journal of Neuroscience Methods },
  issn = { 1872678X },
  doi = { 10.1016/j.jneumeth.2015.03.005 },
  author = { Jones and Liu and Cohan and Ellisman and Tasdizen },
  abstract = { Background: In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. New method: We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. Results: We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. Comparison with existing methods: Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method. Conclusion: Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70{\%} time improvement when compared with other examples in publication. },
}

@article{Fehr2015,
  year = { 2015 },
  volume = { 112 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images },
  pages = { E6265--E6273 },
  number = { 46 },
  keywords = { Gleason score classification,Learning from unbalanced data,Multiparametric mri,PCa gleason (3+4) vs. (4+3) cancers,PCa gleason 6 vs. ≥7 },
  journal = { Proceedings of the National Academy of Sciences of the United States of America },
  issn = { 10916490 },
  doi = { 10.1073/pnas.1505935112 },
  author = { Fehr and Veeraraghavan and Wibmer and Gondo and Matsumoto and Vargas and Sala and Hricak and Deasy },
  abstract = { Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93{\%} accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92{\%} for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92{\%} accuracy for cancers occurring in both the PZ and TZ and with 93{\%} for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58{\%} for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63{\%} for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59{\%} for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60{\%} for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns. },
}

@article{DeLeener2015,
  year = { 2015 },
  volume = { 34 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled with Vertebral Labeling },
  pages = { 1705--1718 },
  number = { 8 },
  keywords = { Automatic segmentation,CSF,MRI,Vertebral labeling,deformable model,spinal canal,spinal cord },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 1558254X },
  doi = { 10.1109/TMI.2015.2437192 },
  author = { {De Leener} and Cohen-Adad and Kadoury },
  abstract = { Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g.,T1-,T2-and T2z.ast;-weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter-and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts. },
}

@article{Captur2015,
  year = { 2015 },
  volume = { 17 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Fractal frontiers in cardiovascular magnetic resonance: Towards clinical implementation },
  pages = { 10 },
  number = { 1 },
  keywords = { Cardiovascular magnetic resonance,Image processing,Segmentation },
  journal = { Journal of Cardiovascular Magnetic Resonance },
  issn = { 1532429X },
  doi = { 10.1186/s12968-015-0179-0 },
  author = { Captur and Karperien and Li and Zemrak and Tobon-Gomez and Gao and Bluemke and Elliott and Petersen and Moon },
  abstract = { Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain. This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community. By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools. },
}

@article{Bajcsy2015,
  year = { 2015 },
  volume = { 16 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Survey statistics of automated segmentations applied to optical imaging of mammalian cells },
  pages = { 28 },
  number = { 1 },
  keywords = { Accelerated execution of segmentation for high-thr,Cell segmentation,Cellular measurements,Segmentation evaluation,Segmented objects },
  journal = { BMC Bioinformatics },
  issn = { 14712105 },
  doi = { 10.1186/s12859-015-0762-2 },
  author = { Bajcsy and Cardone and Chalfoun and Halter and Juba and Kociolek and Majurski and Peskin and Simon and Simon and Vandecreme and Brady },
  abstract = { Background: The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. Methods: We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. Results: The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. Conclusions: The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html. },
}

@article{RN949,
  year = { 2015 },
  volume = { 309 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949526543{\&}doi=10.1152{\%}2Fajplung.00410.2014{\&}partnerID=40{\&}md5=56c6058ebfd732edb1588ab2e55b6ec9 },
  type = { Journal Article },
  title = { Estimation of the number of alveolar capillaries by the euler number (Euler-poincar{\'{e}} characteristic) },
  pages = { L1286--L1293 },
  number = { 11 },
  keywords = { Capillary number,Euler number,Stereology },
  journal = { American Journal of Physiology - Lung Cellular and Molecular Physiology },
  issn = { 15221504 },
  doi = { 10.1152/ajplung.00410.2014 },
  author = { Willf{\"{u}}hr and Brandenberger and Piatkowski and Grothausmann and Nyengaard and Ochs and M{\"{u}}hlfeld },
  abstract = { The lung parenchyma provides a maximal surface area of blood-containing capillaries that are in close contact with a large surface areaof the air-containing alveoli. Volume and surface area of capillaries are the classic stereological parameters to characterize the alveolar capillary network (ACN) and have provided essential structure-function information of the lung. When loss (rarefaction) or gain (angiogenesis) of capillaries occurs, these parameters may not be sufficient to provide mechanistic insight. Therefore, it would be desirable to estimate the number of capillaries, as it contains more distinct and mechanistically oriented information. Here, we present a new stereological method to estimate the number of capillary loops in the ACN. One advantage of this method is that it is independent of the shape, size, or distribution of the capillaries. We used consecutive, 1 {\_}mthick sections from epoxy resin-embedded material as a physical disector. The Euler-Poincar{\'{e}} characteristic of capillary networks can be estimated by counting the easily recognizable topolog ical constellations of “islands,” “bridges,” and “holes.” The total number of capillary loops in the ACN can then be calculated from the Euler- Poincar{\'{e}} characteristic. With the use of the established estimator of alveolar number, it is possible to obtain the mean number of capillary loops per alveolus. In conclusion, estimation of alveolar capillaries by design-based stereology is an efficient and unbiased method to characterize the ACN and may be particularly useful for studies on emphysema, pulmonary hypertension, or lung development. },
}

@article{RN803,
  year = { 2015 },
  volume = { 24 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation },
  pages = { 5696--5705 },
  number = { 12 },
  keywords = { Gray-scale,Image segmentation,Mathematical model,Noise,Surface morphology,Surface topography,Three-dimensional displays },
  journal = { IEEE Transactions on Image Processing },
  issn = { 10577149 },
  doi = { 10.1109/TIP.2015.2484061 },
  author = { Turpeinen and Myllys and Kekalainen and Timonen },
  abstract = { We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, and its efficient discretization. },
}

@article{RN781,
  year = { 2015 },
  volume = { 42 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Enhancing atlas based segmentation with multiclass linear classifiers },
  pages = { 7169--7181 },
  number = { 12 },
  keywords = { atlas based segmentation,machine learning,multiple atlas,support vector machine },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4935946 },
  author = { Sdika },
  abstract = { Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coup{\'{e}} or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy. },
}

@article{RN947,
  year = { 2015 },
  volume = { 34 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920159378{\&}doi=10.1109{\%}2FTMI.2014.2343936{\&}partnerID=40{\&}md5=a81867f10d05f5ddb53549f54f45df01 },
  type = { Journal Article },
  title = { Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery },
  pmid = { 25095250 },
  pages = { 49--60 },
  number = { 1 },
  keywords = { Anatomical overlay,Procedure guidance,computed tomography (CT),model-based cardiac image registration },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 1558254X },
  doi = { 10.1109/TMI.2014.2343936 },
  author = { Neumann and Grbic and John and Navab and Hornegger and Ionasec },
  abstract = { Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors. },
}

@article{RN945,
  year = { 2015 },
  volume = { 107 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971597818{\&}partnerID=40{\&}md5=313cdcaa420e54b4e9dad1c9f2228cc8 },
  type = { Journal Article },
  title = { Development of cloud software services for computational analysis of blood flows },
  keywords = { ANSYS Fluent,Aortic valve,Blood flows,Cloud computing,OpenStack,Performance analysis,Software as a service },
  journal = { Civil-Comp Proceedings },
  issn = { 17593433 },
  doi = { 10.4203/ccp.107.23 },
  author = { Kaceniauskas and Pacevic and Sta{\v{s}}kuniene and Starikovicius and Davidavicius },
  abstract = { This paper presents the development of the cloud software services for computational analysis of blood flows on a private university cloud. The main focus is on the software service level built on the top of the computational platform provided. Moreover, user friendly management tools have been developed by using the Apache jclouds API to enhance the management of OpenStack cloud infrastructure and to increase the accessibility of engineering software. The blood flow through an aortic valve is considered as a pilot application of the private cloud infrastructure. The investigated flows can be described using numerical models based on viscous incompressible Navier-Stokes equations. The modelling software environment based on ANSYS Fluent is developed as a software service (SaaS) for the numerical analysis of low flow, low pressure gradient aortic stenosis. The performance of the developed cloud infrastructure has been assessed testing CPU, memory IO, disk IO, network and the developed software service for computations of blood flow through an aortic valve. The results obtained have been compared with the performance obtained using the native hardware. },
}

@article{RN941,
  year = { 2015 },
  volume = { 73 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920973040{\&}doi=10.1016{\%}2Fj.bone.2014.12.061{\&}partnerID=40{\&}md5=ce16efe68357b4c7c5a21d428c6efe79 },
  type = { Journal Article },
  title = { $\mu$CT-based, in vivo dynamic bone histomorphometry allows 3D evaluation of the early responses of bone resorption and formation to PTH and alendronate combination therapy },
  pages = { 198--207 },
  keywords = { Animal models/rodent,Anti-resorptive treatment,Bone stiffness,In vivo $\mu$CT,Parathyroid hormone,Trabecular bone microstructure },
  journal = { Bone },
  issn = { 87563282 },
  doi = { 10.1016/j.bone.2014.12.061 },
  author = { Bakker and Altman and Tseng and Tribble and Li and Chandra and Qin and Liu },
  abstract = { Current osteoporosis treatments improve bone mass by increasing net bone formation: anti-resorptive drugs such as bisphosphonates block osteoclast activity, while anabolic agents such as parathyroid hormone (PTH) increase bone remodeling, with a greater effect on formation. Although these drugs are widely used, their role in modulating formation and resorption is not fully understood, due in part to technical limitations in the ability to longitudinally assess bone remodeling. Importantly, it is not known whether or not PTH-induced bone formation is independent of resorption, resulting in controversy over the effectiveness of combination therapies that use both PTH and an anti-resorptive. In this study, we developed a $\mu$CT-based, in vivo dynamic bone histomorphometry technique for rat tibiae, and applied this method to longitudinally track changes in bone resorption and formation as a result of treatment with alendronate (ALN), PTH, or combination therapy of both PTH and ALN (PTH+ALN). Correlations between our $\mu$CT-based measures of bone formation and measures of bone formation based on calcein-labeled histology (r = 0.72-0.83) confirm the accuracy of this method. Bone remodeling parameters measured through $\mu$CT-based in vivo dynamic bone histomorphometry indicate an increased rate of bone formation in rats treated with PTH and PTH+ALN, together with a decrease in bone resorption measures in rats treated with ALN and PTH+ALN. These results were further supported by traditional histology-based measurements, suggesting that PTH was able to induce bone formation while bone resorption was suppressed. },
}

@inproceedings{kim2015efficient,
  year = { 2015 },
  title = { Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources },
  pages = { 54--61 },
  organization = { Springer, Cham },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Workshop on Clinical Image-Based Procedures/Kim et al. - 2015 - Efficient and extensible workflow Reliable whole brain segmentation for large-scale, multi-center longitudinal human.pdf:pdf },
  booktitle = { Workshop on Clinical Image-Based Procedures },
  author = { Kim and Nopoulos and Paulsen and Johnson },
}

@inproceedings{Tourbier2015a,
  year = { 2015 },
  volume = { 9413 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943378886{\&}doi=10.1117{\%}2F12.2081777{\&}partnerID=40{\&}md5=1e5911cbeee0e1cf86d55e9988c697b9 },
  type = { Conference Proceedings },
  title = { Automatic brain extraction in fetal MRI using multi-atlas-based segmentation },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 94130Y },
  issn = { 16057422 },
  isbn = { 9781628415032 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2081777 },
  booktitle = { Medical Imaging 2015: Image Processing },
  author = { Tourbier and Hagmann and Cagneaux and Guibaud and Gorthi and Schaer and Thiran and Meuli and {Bach Cuadra} },
  address = { Bellingham },
  abstract = { {\textcopyright} 2015 SPIE. In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality. },
}

@inproceedings{Khajwaniya,
  year = { 2015 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959108119{\&}doi=10.1109{\%}2FISCO.2015.7282324{\&}partnerID=40{\&}md5=3cac197663c657dd57bf87eb47941101 },
  type = { Conference Proceedings },
  title = { Satellite image denoising using Weiner filter with SPEA2 algorithm },
  keywords = { Bilateral Filter,Image denoising,SPEA2 Algorithm,Weiner Filter },
  isbn = { 9781479964802 },
  doi = { 10.1109/ISCO.2015.7282324 },
  booktitle = { Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015 },
  author = { Khajwaniya and Tiwari },
  abstract = { The paper proposes a new methodology in order to improve the quality of satellite images. It is demonstrated that it is possible to achieve a better performance than that of Bilateral filter in a variety of noise levels. We have proposed Weiner filter in accordance with SPEA2 algorithm which removes pre-filtering and high noise level: therefore it improves the Peak Signal-to-Noise Ratio (PSNR) and visual quality gets improved and complexities and processing time are reduced. This improved algorithm is extended and used to denoise satellite images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite. },
}

@inproceedings{RN944,
  year = { 2015 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84970959837{\&}partnerID=40{\&}md5=0d3090fef893d468c72b58001dc5eceb },
  type = { Conference Proceedings },
  title = { Contrast-to-noise based metric of denoising algorithms for liver vein segmentation },
  publisher = { SciTePress },
  pages = { 59--67 },
  keywords = { CUDA,Contrast to noise ratio,Fast marching,GPGPU,Geodesic active contours,Liver,Proximal algorithms,Total variance de-noising,Vessels segmentation,Xeon phi },
  isbn = { 9789897581182 },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.5220/0005542400590067 },
  booktitle = { SIGMAP 2015 - 12th International Conference on Signal Processing and Multimedia Applications, Proceedings; Part of 12th International Joint Conference on e-Business and Telecommunications, ICETE 2015 },
  author = { Nikonorov and Kolsanov and Petrov and Yuzifovich and Prilepin and Bychenkov },
  abstract = { We analyse CT image denoising when applied to vessel segmentation. Proposed semi-global quality metric based on the contrast-to-noise ratio allowed us to estimate initial image quality and efficiency of denoising procedures without prior knowledge about a noise-free image. We show that the total variance filtering in L1 metric provides the best denoising when compared to other well-known denoising procedures such as nonlocal means denoising or anisotropic diffusion. Computational complexity of this denoising algorithm is addressed by comparing its implementation for Intel MIC and for NVIDIA CUDA HPC systems. },
}

@inproceedings{RN937,
  year = { 2015 },
  volume = { 2015-Decem },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958176573{\&}doi=10.1109{\%}2FIROS.2015.7353829{\&}partnerID=40{\&}md5=7513ff75c060dfaaf113a4487f0d9a72 },
  type = { Conference Proceedings },
  title = { Motion planning for a three-stage multilumen transoral lung access system },
  publisher = { Institute of Electrical and Electronics Engineers Inc. },
  pages = { 3255--3261 },
  keywords = { Biomedical imaging,Biopsy,Electron tubes,Lungs,Needles,Planning,Robots },
  issn = { 21530866 },
  isbn = { 9781479999941 },
  doi = { 10.1109/IROS.2015.7353829 },
  booktitle = { IEEE International Conference on Intelligent Robots and Systems },
  author = { Kuntz and Torres and Feins and Webster and Alterovitz },
  abstract = { Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios. },
}

@inproceedings{RN943,
  year = { 2015 },
  volume = { 0 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012231802{\&}doi=10.1007{\%}2F978-3-662-46224-9{\_}71{\&}partnerID=40{\&}md5=a2320e0e42ae26842cd9d13f26bc03b8 },
  type = { Conference Proceedings },
  title = { Band-pass filter design by segmentation in frequency domain for detection of epithelial cells in endomicroscope images },
  publisher = { Kluwer Academic Publishers },
  pages = { 413--418 },
  issn = { 1431472X },
  isbn = { 9783662462232 },
  editor = { [object Object],[object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-662-46224-9_71 },
  booktitle = { Informatik aktuell },
  author = { Bier and Mualla and Steidl and Bohr and Neumann and Maier and Hornegger },
  abstract = { Voice hoarseness can have various reasons, one of them is a change of the vocal fold mucus. This change can be examined with micro endoscopes. Cell detection in these images is a difficult task, due to bad image quality, caused by noise and illumination variations. In previous works, it was observed that the repetitive pattern of the cell walls cause an elliptical shape in the Fourier domain [1, 2]. A manual segmentation and back transformation of this shape results in filtered images, where the cell detection is much easier [3]. The goal of this work is to automatically segment the elliptical shape in Fourier domain. Two different approaches are developed to get a suitable band-pass filter: a thresholding and an active contour method. After the band-pass filter is applied, the achieved results are superior to the manual segmentation case. },
}

@inbook{Tourbier2014,
  year = { 2014 },
  volume = { 8674 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction },
  series = { Lecture Notes in Computer Science },
  publisher = { Springer-Verlag Berlin },
  pages = { 252--259 },
  isbn = { 978-3-319-10470-6; 978-3-319-10469-0 },
  editor = { [object Object],[object Object],[object Object],[object Object],[object Object] },
  booktitle = { Medical Image Computing and Computer-Assisted Intervention - Miccai 2014, Pt Ii },
  author = { Tourbier and Bresson and Hagmann and Thiran and Meuli and Cuadra },
  address = { Berlin },
}

@inbook{Zhou2014,
  year = { 2014 },
  volume = { 442 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Comparison and assessment of different image registration algorithms based on ITK },
  series = { Applied Mechanics and Materials },
  publisher = { Trans Tech Publications Ltd },
  pages = { 515--519 },
  keywords = { Algorithms comparison,ITK,Image registration,Registration framework },
  issn = { 16609336 },
  isbn = { 9783037859018 },
  editor = { [object Object] },
  doi = { 10.4028/www.scientific.net/AMM.442.515 },
  booktitle = { Applied Mechanics and Materials },
  author = { Zhou },
  address = { Durnten-Zurich },
  abstract = { A lot of image registration algorithms are proposed in recent year, among these algorithms, which one is better or faster than the other can be only validated by experiments. In this paper, ITK (Insight Segmentation and Registration Toolkit) is used for verifying different algorithms as a framework. ITK framework requires the following components: a fixed image, a moving image, a transform, a metric, an interpolator and an optimizer. Dozens of classical algorithms are tested under the same conditions and their experimental results are demonstrated with different metrics, interpolators or optimizers. By comparison of registration time and accuracy, those practical and useful algorithms are selected for developing software in image analysis. These kinds of experiments are very valuable for software engineering, they can shorten the cycle of software development and greatly reduce the development costs. {\textcopyright} (2014) Trans Tech Publications, Switzerland. },
}

@article{oguz2014dtiprep,
  year = { 2014 },
  volume = { 8 },
  url = { http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3906573{\&}tool=pmcentrez{\&}rendertype=abstract http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00004/full http://journal.frontiersin.org/article/10.3389/fninf.2014.00004/abstract{\%}0Ahttp://www },
  title = { DTIPrep: quality control of diffusion-weighted images },
  publisher = { Frontiers Media SA },
  pmid = { 24523693 },
  pages = { 4 },
  number = { January },
  keywords = { diffusion MRI,diffusion mri,diffusion tensor imaging,open-source,preprocessing,quality c,quality control,software },
  journal = { Frontiers in neuroinformatics },
  issn = { 1662-5196 },
  isbn = { 1662-5196 (Electronic){\$}\backslash{\$}r1662-5196 (Linking) },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Oguz et al/Frontiers in neuroinformatics/Oguz et al. - 2014 - DTIPrep quality control of diffusion-weighted images.pdf:pdf },
  doi = { 10.3389/fninf.2014.00004 },
  author = { Oguz and Farzinfar and Matsui and Budin and Liu and Gerig and Johnson and Styner },
  annote = { From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

{\#}{\#}CONTRIBUTIONS: As a member of the National Alliance for Medical Imaging Computing (NAMIC) I collaborated on many software engineering projets.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#} },
  abstract = { In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis. },
}

@article{Kim2014,
  year = { 2014 },
  volume = { 32 },
  url = { http://www.ncbi.nlm.nih.gov/pubmed/24818817 },
  title = { Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data },
  publisher = { Elsevier Inc. },
  pmid = { 24818817 },
  pages = { 832--844 },
  number = { 7 },
  month = { may },
  keywords = { Machine learning,Multicenter study,Random forest,Segmentation },
  journal = { Magnetic Resonance Imaging },
  issn = { 18735894 },
  isbn = { 3193213152 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Magnetic Resonance Imaging/Kim et al. - 2014 - Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data.pdf:pdf },
  doi = { 10.1016/j.mri.2014.04.016 },
  author = { Kim and Magnotta and Liu and Johnson },
  annote = { From Duplicate 1 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.; Kim, Eun Young; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

From Duplicate 1 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.; Kim, Eun Young; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#} },
  abstract = { Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n{\textgreater}. 3000). Less than 10{\%} of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen. {\textcopyright} 2014 Elsevier Inc. },
}

@article{Pennati2014a,
  year = { 2014 },
  volume = { 273 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910050100{\&}doi=10.1148{\%}2Fradiol.14132470{\&}partnerID=40{\&}md5=75e8d2666ef74110c8fda5fcbf082a75 },
  type = { Journal Article },
  title = { Assessment of regional lung function with multivolume 1H MR imaging in health and obstructive lung Disease: Comparison with 3He MR imaging },
  pages = { 580--590 },
  number = { 2 },
  journal = { Radiology },
  issn = { 15271315 },
  doi = { 10.1148/radiol.14132470 },
  author = { Pennati and Quirk and Yablonskiy and Castro and Aliverti and Woods },
  abstract = { Purpose: To introduce a method based on multivolume proton (hydrogen [1H]) magnetic resonance (MR) imaging for the regional assessment of lung ventilatory function, investigating its use in healthy volunteers and patients with obstructive lung disease and comparing the outcome with the outcome of the research standard helium 3 (3He) MR imaging.
Materials and Methods: The institutional review board approved the HIPAA-compliant protocol, and informed written consent was obtained from each subject. Twenty-six subjects, including healthy volunteers (n = 6) and patients with severe asthma (n = 11) and mild (n = 6) and severe (n = 3) emphysema, were imaged with a 1.5-T whole-body MR unit at four lung volumes (residual volume [RV], functional residual capacity [FRC], 1 L above FRC [FRC+1 L], total lung capacity [TLC]) with breath holds of 10-11 seconds, by using volumetric interpolated breath-hold examination. Each pair of volumes were registered, resulting in maps of 1H signal change between the two lung volumes. 3He MR imaging was performed at FRC+1 L by using a two-dimensional gradient-echo sequence. 1H signal change and 3He signal were measured and compared in corresponding regions of interest selected in ventral, intermediate, and dorsal areas.
Results: In all volunteers and patients combined, proton signal difference between TLC and RV correlated positively with 3He signal (correlation coefficient R2 = 0.64, P {\textless} .001). Lower (P {\textless} .001) but positive correlation results from 1H signal difference between FRC and FRC+1 L (R2 = 0.44, P {\textless} .001). In healthy volunteers, 1H signal changes show a higher median and interquartile range compared with patients with obstructive disease and significant differences between nondependent and dependent regions.
Conclusion: Findings in this study demonstrate that multivolume 1H MR imaging, without contrast material, can be used as a biomarker for regional ventilation, both in healthy volunteers and patients with obstructive lung disease. },
}

@article{Hesse2014,
  year = { 2014 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Alterations of mass density and 3D osteocyte lacunar properties in bisphosphonate-related osteonecrotic human jaw bone, a synchrotron $\mu$CT study },
  pages = { 11 },
  number = { 2 },
  journal = { PLoS ONE },
  issn = { 19326203 },
  doi = { 10.1371/journal.pone.0088481 },
  author = { Hesse and Langer and Varga and Pacureanu and Dong and Schrof and Man̈nicke and Suhonen and Olivier and Maurer and Kazakia and Raum and Peyrin },
  abstract = { Osteonecrosis of the jaw, in association with bisphosphonates (BRONJ) used for treating osteoporosis or cancer, is a severe and most often irreversible side effect whose underlying pathophysiological mechanisms remain largely unknown. Osteocytes are involved in bone remodeling and mineralization where they orchestrate the delicate equilibrium between osteoclast and osteoblast activity and through the active process called osteocytic osteolysis. Here, we hypothesized that (i) changes of the mineralized tissue matrix play a substantial role in the pathogenesis of BRONJ, and (ii) the osteocyte lacunar morphology is altered in BRONJ. Synchrotron $\mu$CT with phase contrast is an appropriate tool for assessing both the 3D morphology of the osteocyte lacunae and the bone matrix mass density. Here, we used this technique to investigate the mass density distribution and 3D osteocyte lacunar properties at the sub-micrometer scale in human bone samples from the jaw, femur and tibia. First, we compared healthy human jaw bone to human tibia and femur in order to assess the specific differences and address potential explanations of why the jaw bone is exclusively targeted by the necrosis as a side effect of BP treatment. Second, we investigated the differences between BRONJ and control jaw bone samples to detect potential differences which could aid an improved understanding of the course of BRONJ. We found that the apparent mass density of jaw bone was significantly smaller compared to that of tibia, consistent with a higher bone turnover in the jaw bone. The variance of the lacunar volume distribution was significantly different depending on the anatomical site. The comparison between BRONJ and control jaw specimens revealed no significant increase in mineralization after BP. We found a significant decrease in osteocyte-lacunar density in the BRONJ group compared to the control jaw. Interestingly, the osteocyte-lacunar volume distribution was not altered after BP treatment. {\textcopyright} 2014 Hesse et al. },
}

@article{Spadea2014,
  year = { 2014 },
  volume = { 90 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Contrast-enhanced proton radiography for patient set-up by using x-ray CT prior knowledge },
  pages = { 628--636 },
  number = { 3 },
  journal = { International Journal of Radiation Oncology Biology Physics },
  issn = { 1879355X },
  doi = { 10.1016/j.ijrobp.2014.06.057 },
  author = { Spadea and Fassi and Zaffino and Riboldi and Baroni and Depauw and Seco },
  abstract = { Methods and Materials: Six lung cancer patients CT scans were preprocessed by masking out the gross tumor volume (GTV), and digitally reconstructed radiographs along the planned beams eye view (BEV) were generated, for a total of 27 projections. Proton radiographies (PR) were also computed for the same BEV through Monte Carlo simulations. The digitally reconstructed radiograph was subtracted from the corresponding proton image, resulting in a contrast-enhanced proton radiography (CEPR). Michelson contrast analysis was performed both on PR and CEPR. The tumor region was then automatically segmented on CEPR and compared to the ground truth (GT) provided by physicians in terms of Dice coefficient, accuracy, precision, sensitivity, and specificity.
Purpose: To obtain a contrasted image of the tumor region during the setup for proton therapy in lung patients, by using proton radiography and x-ray computed tomography (CT) prior knowledge.
Results: Contrast on CEPR was, on average, 4 times better than on PR. For 10 lateral projections (±45 off of 90 or 270), although it was not possible to distinguish the tumor region in the PR, CEPR offers excellent GTV visibility. The median ± quartile values of Dice, precision, and accuracy indexes were 0.86 ± 0.03, 0.86 ± 0.06, and 0.88 ± 0.02, respectively, thus confirming the reliability of the method in highlighting tumor boundaries. Sensitivity and specificity analysis demonstrated that there is no systematic over- or underestimation of the tumor region. Identification of the tumor boundaries using CEPR resulted in a more accurate and precise definition of GTV compared to that obtained from pretreatment CT.
Conclusions: In most proton centers, the current clinical protocol is to align the patient using kV imaging with bony anatomy as a reference. We demonstrated that CEPR can significantly improve tumor visualization, allowing better patient set-up and permitting image guided proton therapy (IGPT). },
}

@article{Rusu2014,
  year = { 2014 },
  volume = { 41 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Prostatome: A combined anatomical and disease based MRI atlas of the prostate },
  pages = { 12 },
  number = { 7 },
  keywords = { anatomic imaging atlas,guided biopsy,image processing,in vivo imaging,prostate cancer },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4881515 },
  author = { Rusu and Bloch and Jaffe and Genega and Lenkinski and Rofsky and Feleppa and Madabhushi },
  abstract = { Purpose: In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap. Methods: The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides " ground truth" mapping of cancer extent on in vivo imaging for 23 subjects. Results: AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90{\%}, and prostate DSC of 88{\%}, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage. Conclusions: The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies. {\textcopyright} 2014 American Association of Physicists in Medicine. },
}

@article{Pennati2014,
  year = { 2014 },
  volume = { 21 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Comparison Between Multivolume CT-Based Surrogates of Regional Ventilation in Healthy Subjects },
  pages = { 1268--1275 },
  number = { 10 },
  keywords = { CT imaging,Gravity,Healthy lung,Ventilation },
  journal = { Academic Radiology },
  issn = { 18784046 },
  doi = { 10.1016/j.acra.2014.05.022 },
  author = { Pennati and Salito and Baroni and Woods and Aliverti },
  abstract = { Rationale and Objectives: The assessment of regional ventilation is of critical importance when investigating lung function during disease progression and planning of pulmonary interventions. Recently, different computed tomography (CT)-based parameters have been proposed as surrogates of lung ventilation. The aim of the present study was to compare these parameters, namely variations of density ($\delta$HU), specific volume (sVol), and specific gas volume ($\delta$SVg) between different lung volumes, in relation to their topographic distribution within the lung. Materials and Methods: Ten healthy volunteers were scanned via high-resolution CT at residual volume (RV) and total lung capacity (TLC); $\delta$HU, sVol, and $\delta$SVg were mapped voxel by voxel after registering TLC onto RV. Variations of the three parameters along the vertical and horizontal directions were analyzed. Results: Along the vertical direction (from ventral to dorsal regions), a strong dependence on gravity was found in $\delta$HU and sVol, with greater values in the dorsal regions of the lung (P{\textless}.001), whereas $\delta$SVg was more homogeneously distributed within the lung. Conversely, along the caudocranial direction (from lung bases to apexes) where no gravitational gradient is present, the three parameters behaved similarly, with lower values at the apices. Conclusions: $\delta$HU, sVol, and $\delta$SVg behave differently along the gravity direction. As the greater amount of air delivered to the dependent portion of the lung supplies a larger number of alveoli, the amount of gas delivered to alveoli compared to the mass of tissue is not gravity dependent. The minimization of gravity dependence in the distribution of ventilation when using $\delta$SVg suggests that this parameter is more reliable to discriminate healthy from pathologic regions. },
}

@article{Mattonen2014,
  year = { 2014 },
  volume = { 41 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer },
  pages = { 14 },
  number = { 3 },
  keywords = { cancer recurrence,computed tomography,lung,stereotactic radiation therapy,texture analysis },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4866219 },
  author = { Mattonen and Palma and Haasbeek and Senan and Ward },
  abstract = { Purpose: Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment. Methods: A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three-dimensional (3D) consolidative and ground-glass opacity (GGO) changes were manually delineated on all follow-up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first-order features [mean density and standard deviation of density (first-order texture)], and 7 second-order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two-fold cross validation (CV). Results: For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months post-treatment were binned based on their recorded scan dates into 2-5 month and 5-8 month follow-up time ranges. At 2-5 months post-treatment, first-order texture, energy, and entropy provided AUCs of 0.79-0.81 using a linear classifier. On two-fold CV, first-order texture yielded 73{\%} accuracy versus 76{\%}-77{\%} with the second-order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two-fold CV accuracies of 60{\%} and 57{\%}, and AUCs of 0.72 and 0.65, respectively. Conclusions: Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer-aided diagnosis tool which could provide earlier salvage of patients with recurrence. {\textcopyright} 2014 American Association of Physicists in Medicine. },
}

@article{Liu2014,
  year = { 2014 },
  volume = { 226 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A modular hierarchical approach to 3D electron microscopy image segmentation },
  pages = { 88--102 },
  keywords = { Electron microscopy,Hierarchical segmentation,Image segmentation,Neuron reconstruction,Semi-automatic segmentation },
  journal = { Journal of Neuroscience Methods },
  issn = { 01650270 },
  doi = { 10.1016/j.jneumeth.2014.01.022 },
  author = { Liu and Jones and Seyedhosseini and Tasdizen },
  abstract = { The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. {\textcopyright} 2014 Elsevier B.V. },
}

@article{Kazemifar2014,
  year = { 2014 },
  volume = { 227 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease },
  pages = { 35--46 },
  keywords = { Alzheimer disease,Hippocampus,MRI,Medial temporal lobe segmentation,Multi-atlas,Shape },
  journal = { Journal of Neuroscience Methods },
  issn = { 1872678X },
  doi = { 10.1016/j.jneumeth.2014.01.033 },
  author = { Kazemifar and Drozd and Rajakumar and Borrie and Bartha },
  abstract = { Background: The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus. Methods: The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change ($\delta$MTL). Baseline and 24 months 3D T1-weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools. Results: The average $\delta$MTL (mean±SEM) was 68±35mm3 in NEC, 187±38mm3 in MCI and 300±34mm3 in the AD group and was significantly different (p{\textless}0.0001) between all three groups. The $\delta$MTL was correlated with cognitive decline. Comparison with existing method(s): Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups. Conclusion: This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups. {\textcopyright} 2014 Elsevier B.V. },
}

@article{Ge2014,
  year = { 2014 },
  volume = { 41 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Toward the development of intrafraction tumor deformation tracking using a dynamic multi-leaf collimator },
  pages = { 10 },
  number = { 6 },
  keywords = { DMLC tracking,adaptation,tumor deformation },
  journal = { Medical Physics },
  issn = { 00942405 },
  doi = { 10.1118/1.4873682 },
  author = { Ge and O'Brien and Shieh and Booth and Keall },
  abstract = { Purpose: Intrafraction deformation limits targeting accuracy in radiotherapy. Studies show tumor deformation of over 10 mm for both single tumor deformation and system deformation (due to differential motion between primary tumors and involved lymph nodes). Such deformation cannot be adapted to with current radiotherapy methods. The objective of this study was to develop and experimentally investigate the ability of a dynamic multi-leaf collimator (DMLC) tracking system to account for tumor deformation. Methods: To compensate for tumor deformation, the DMLC tracking strategy is to warp the planned beam aperture directly to conform to the new tumor shape based on real time tumor deformation input. Two deformable phantoms that correspond to a single tumor and a tumor system were developed. The planar deformations derived from the phantom images in beam's eye view were used to guide the aperture warping. An in-house deformable image registration software was developed to automatically trigger the registration once new target image was acquired and send the computed deformation to the DMLC tracking software. Because the registration speed is not fast enough to implement the experiment in real-time manner, the phantom deformation only proceeded to the next position until registration of the current deformation position was completed. The deformation tracking accuracy was evaluated by a geometric target coverage metric defined as the sum of the area incorrectly outside and inside the ideal aperture. The individual contributions from the deformable registration algorithm and the finite leaf width to the tracking uncertainty were analyzed. Clinical proof-of-principle experiment of deformation tracking using previously acquired MR images of a lung cancer patient was implemented to represent the MRI-Linac environment. Intensity-modulated radiation therapy (IMRT) treatment delivered with enabled deformation tracking was simulated and demonstrated. Results: The first experimental investigation of adapting to tumor deformation has been performed using simple deformable phantoms. For the single tumor deformation, the A u+Ao was reduced over 56{\%} when deformation was larger than 2 mm. Overall, the total improvement was 82{\%}. For the tumor system deformation, the Au+Ao reductions were all above 75{\%} and the total Au+Ao improvement was 86{\%}. Similar coverage improvement was also found in simulating deformation tracking during IMRT delivery. The deformable image registration algorithm was identified as the dominant contributor to the tracking error rather than the finite leaf width. The discrepancy between the warped beam shape and the ideal beam shape due to the deformable registration was observed to be partially compensated during leaf fitting due to the finite leaf width. The clinical proof-of-principle experiment demonstrated the feasibility of intrafraction deformable tracking for clinical scenarios. Conclusions: For the first time, we developed and demonstrated an experimental system that is capable of adapting the MLC aperture to account for tumor deformation. This work provides a potentially widely available management method to effectively account for intrafractional tumor deformation. This proof-of-principle study is the first experimental step toward the development of an image-guided radiotherapy system to treat deforming tumors in real-time. {\textcopyright} 2014 American Association of Physicists in Medicine. },
}

@article{Ertz2014,
  year = { 2014 },
  volume = { 8 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919960299{\&}doi=10.5311{\%}2FJOSIS.2014.8.182{\&}partnerID=40{\&}md5=a7c671f5acc3d9aeaea0fe07a547c7fb },
  type = { Journal Article },
  title = { The open source dynamics in geospatial research and education },
  pages = { 67--71 },
  number = { 2014 },
  journal = { Journal of Spatial Information Science },
  issn = { 1948660X },
  doi = { 10.5311/JOSIS.2014.8.182 },
  author = { Ertz and Rey and Joost },
}

@article{DeLeener2014,
  year = { 2014 },
  volume = { 98 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Robust, accurate and fast automatic segmentation of the spinal cord },
  pages = { 528--536 },
  keywords = { Automatic,Deformable model,MRI,Propagation,Spinal cord segmentation },
  journal = { NeuroImage },
  issn = { 10959572 },
  doi = { 10.1016/j.neuroimage.2014.04.051 },
  author = { {De Leener} and Kadoury and Cohen-Adad },
  abstract = { Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord. {\textcopyright} 2014 Elsevier Inc. },
}

@article{Avants2014,
  year = { 2014 },
  volume = { 8 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { The Insight ToolKit image registration framework },
  pages = { 13 },
  number = { APR },
  keywords = { Brain,Death,MRI,Open-source,Registration },
  journal = { Frontiers in Neuroinformatics },
  issn = { 16625196 },
  doi = { 10.3389/fninf.2014.00044 },
  author = { Avants and Tustison and Stauffer and Song and Wu and Gee },
  abstract = { Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK4) seeks to establish new standards in publicly available image registration methodology. ITK4 makes several advances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling. {\textcopyright} 2014 Avants, Tustison, Stauffer, Song, Wuand Gee. },
}

@article{Adebar2014,
  year = { 2014 },
  volume = { 61 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { 3-D ultrasound-guided robotic needle steering in biological tissue },
  pages = { 2899--2910 },
  number = { 12 },
  keywords = { Image-guided intervention,robotic needle steering,ultrasound Doppler,ultrasound imaging },
  journal = { IEEE Transactions on Biomedical Engineering },
  issn = { 15582531 },
  doi = { 10.1109/TBME.2014.2334309 },
  author = { Adebar and Fletcher and Okamura },
  abstract = { Robotic needle steering systems have the potential to greatly improve medical interventions, but they require new methods for medical image guidance. Three-dimensional (3-D) ultrasound is a widely available, low-cost imaging modality that may be used to provide real-time feedback to needle steering robots. Unfortunately, the poor visibility of steerable needles in standard grayscale ultrasound makes automatic segmentation of the needles impractical. A new imaging approach is proposed, in which high-frequency vibration of a steerable needle makes it visible in ultrasound Doppler images. Experiments demonstrate that segmentation from this Doppler data is accurate to within 1-2 mm. An image-guided control algorithm that incorporates the segmentation data as feedback is also described. In experimental tests in ex vivo bovine liver tissue, a robotic needle steering system implementing this control scheme was able to consistently steer a needle tip to a simulated target with an average error of 1.57 mm. Implementation of 3-D ultrasound-guided needle steering in biological tissue represents a significant step toward the clinical application of robotic needle steering. },
}

@incollection{Zhang2014a,
  year = { 2014 },
  volume = { 8789 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911390478{\&}partnerID=40{\&}md5=d52525096590e132a0f2bdfbee3e3a8e },
  type = { Serial },
  title = { The MAP client: User-friendly musculoskeletal modelling workflows },
  pages = { 182--192 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Zhang and Sorby and Clement and Thomas and Hunter and Nielsen and Lloyd and Taylor and Besier },
}

@incollection{Johnson2014a,
  year = { 2014 },
  title = { The ITK Software Guide Book 2 : Design and Functionality Fourth Edition Updated for ITK version 4.6 },
  pages = { 805 },
  keywords = { Guide,Registration,Segmentation },
  isbn = { 978-1-930934-28-3 },
  doi = { 1�930934-15�7 },
  booktitle = { The ITK Software GUIDE },
  author = { Johnson and Ib and Mccormick and Consortium },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environment known as CMake to manage platform-specific project generation and compilation process in a platform-independent way. ITK is implemented in C++. ITK's implementation style employs generic programming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template. The use of C++ templating means that the code is highly efficient and many issues are discovered at compile- time, rather than at run-time during program execution. It also means that many of ITK's algorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high-level programming language Python. This enables rapid prototyping and faster exploration of ideas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is available for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, and extend the software because ITK is an open-source project. ITK uses a model of software development known as Extreme Programming. Extreme Programming collapses the usual software development methodology into a simultaneous iterative process of design-implement-test-release. The key features of Extreme Programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated continuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf. This book is a guide to developing soft- ware with ITK; it is the first of two companion books. This book covers building and installation, general architecture and design, as well as the process of contributing in the ITK community. The second book covers detailed design and functionality for reading and and writing images, filtering, registration, segmentation, and performing statistical analysis. },
}

@incollection{Zhang2014,
  year = { 2014 },
  volume = { 8789 },
  url = { {\%}3CGo to https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911390478{\&}partnerID=40{\&}md5=d52525096590e132a0f2bdfbee3e3a8e },
  type = { Serial },
  title = { The MAP client: User-friendly musculoskeletal modelling workflows },
  series = { Lecture Notes in Computer Science },
  publisher = { Springer International Publishing Ag },
  pages = { 182--192 },
  keywords = { Biomechanical modelling,Musculoskeletal atlas project,Musculoskeletal modelling,Open source software,Personalised simulation,Pipelines,workflows },
  issn = { 16113349 },
  isbn = { 9783319120560 },
  editor = { [object Object],[object Object] },
  doi = { 10.1007/978-3-319-12057-7_21 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Zhang and Sorby and Clement and Thomas and Hunter and Nielsen and Lloyd and Taylor and Besier },
  address = { Cham },
  abstract = { Subject-specific models of the musculoskeletal system are capable of accurately estimating function and loads and show promise for clinical use. However, creating subject-specific models is time-consuming and requires high levels of expertise. To address these issues, we have developed the open source Musculoskeletal Atlas Project (MAP) Client software. The MAP Client provides a user-friendly interface for creating musculoskeletal modelling workflows using community-created plug-ins. In this paper, we discuss the design of the MAP Client, its plug-in architecture and its integration with the Physiome Model Repository. We demonstrate the use of MAP Client with a subject-specific femur modeling workflow using a set of modular open source plug-ins for image segmentation, landmark prediction, model registration and customisation. Our long-term goal is to foster a community of MAP users and plug-in developers to accelerate the clinical use of computational models. },
}

@incollection{Johnson2014,
  year = { 2014 },
  title = { The ITK Software Guide Book 2 : Design and Functionality Fourth Edition Updated for ITK version 4.6 },
  pages = { 805 },
  keywords = { Guide,Registration,Segmentation },
  isbn = { 978-1-930934-28-3 },
  doi = { 1�930934-15�7 },
  booktitle = { The ITK Software GUIDE },
  author = { Johnson and Ib and Mccormick and Consortium },
  abstract = { The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environment known as CMake to manage platform-specific project generation and compilation process in a platform-independent way. ITK is implemented in C++. ITK's implementation style employs generic programming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template. The use of C++ templating means that the code is highly efficient and many issues are discovered at compile- time, rather than at run-time during program execution. It also means that many of ITK's algorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high-level programming language Python. This enables rapid prototyping and faster exploration of ideas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is available for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, and extend the software because ITK is an open-source project. ITK uses a model of software development known as Extreme Programming. Extreme Programming collapses the usual software development methodology into a simultaneous iterative process of design-implement-test-release. The key features of Extreme Programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated continuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf. This book is a guide to developing soft- ware with ITK; it is the first of two companion books. This book covers building and installation, general architecture and design, as well as the process of contributing in the ITK community. The second book covers detailed design and functionality for reading and and writing images, filtering, registration, segmentation, and performing statistical analysis. },
}

@inproceedings{Kremera,
  year = { 2014 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903775128{\&}doi=10.1109{\%}2FATSIP.2014.6834600{\&}partnerID=40{\&}md5=a48a78e38dae1ddafa3b391003173fad },
  type = { Conference Proceedings },
  title = { Feasibility and preliminary validation of 2D/3D image registration using fixed 2-D X-ray devices in image-guided radiotherapy },
  pages = { 172--176 },
  keywords = { patient positioning,radiotherapy,stereoscopic geometry },
  isbn = { 9781479948888 },
  doi = { 10.1109/ATSIP.2014.6834600 },
  booktitle = { 2014 1st International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2014 },
  author = { Kremer and Giard and Sibomana and Seabra and {De Xivry} and Labarbe and MacQ },
  abstract = { In radiotherapy, fixed 2-D X-ray imaging devices have several advantages compared to gantry-mounted systems, such as less geometrical deformations and the possibility to monitor 3-D markers motion in real-time. However, there is a lack of studies concerning the geometry of these systems. For example, in the case of a non-orthogonal geometry, the effect of the angle between the X-ray axes has not been investigated yet. In the first part of this study, the optimal angle was analyzed theoretically. Results showed that 60° between the axes still enables displacements of the order of 0.35 mm to be detected. In a second step, the performance of the registration method for such oblique configuration was evaluated on phantom data sets. It was found that using images separated by 60° rather than 90° required more than twice as much the number of iterations to obtain sufficient accuracy (i.e. 0.7 mm and 0.5°). {\textcopyright} 2014 IEEE. },
}

@inproceedings{RN951,
  year = { 2014 },
  volume = { 9112 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907042843{\&}doi=10.1117{\%}2F12.2050790{\&}partnerID=40{\&}md5=f686ccba07e29e0052a7bb5edd216e5b },
  type = { Conference Proceedings },
  title = { Software system for computing material and structural properties of bone and muscle in the lower extremity from pQCT },
  publisher = { SPIE },
  pages = { 911216 },
  issn = { 1996756X },
  isbn = { 9781628410495 },
  doi = { 10.1117/12.2050790 },
  booktitle = { Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV },
  author = { Makrogiannis and Ferrucci },
  abstract = { Peripheral Quantitative Computed Tomography (pQCT) is a non-invasive imaging technology that is well-suited for quantification of bone structural and material properties. Because of its increasing use and applicability, the development of automated quantification methods for pQCT images is an appealing field of research. In this paper we introduce a software system for hard and soft tissue quantification in the lower leg using pQCT imaging data. The main stages of our approach are the segmentation and identification of bone, muscle and fat, and the computation of densitometric and geometric variables of each regional tissue type. Our system was validated against reference area and densitometric measurements over a set of test images and produced encouraging results. {\textcopyright} 2014 Copyright SPIE. },
}

@book{Kugu2013,
  year = { 2013 },
  url = { {\%}3CGo to },
  type = { Book },
  title = { Satellite Image Denoising Using Bilateral Filter with SPEA2 Optimized Parameters },
  series = { Proceedings of 6th International Conference on Recent Advances in Space Technologies },
  publisher = { Ieee },
  pages = { 217--223 },
  isbn = { 978-1-4673-6396-9; 978-1-4673-6395-2 },
  author = { Kugu },
  address = { New York },
}

@inbook{Rezaei2013,
  year = { 2013 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Joint registration of attenuation and activity images in gated TOF-PET },
  series = { IEEE Nuclear Science Symposium and Medical Imaging Conference },
  publisher = { Ieee },
  isbn = { 978-1-4799-0534-8 },
  booktitle = { 2013 Ieee Nuclear Science Symposium and Medical Imaging Conference },
  author = { Rezaei and Nuyts and Ieee },
  address = { New York },
}

@inbook{Toledo2013,
  year = { 2013 },
  volume = { 8669 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Super-resolution in cardiac MRI using a Bayesian approach },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 866932 },
  issn = { 16057422 },
  isbn = { 9780819494436 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2007074 },
  booktitle = { Medical Imaging 2013: Image Processing },
  author = { {Velasco Toledo} and Rueda and {Santa Marta} and Romero },
  address = { Bellingham },
  abstract = { Acquisition of proper cardiac MR images is highly limited by continued heart motion and apnea periods. A typical acquisition results in volumes with inter-slice separations of up to 8 mm. This paper presents a super-resolution strategy that estimates a high-resolution image from a set of low-resolution image series acquired in different non- orthogonal orientations. The proposal is based on a Bayesian approach that implements a Maximum a Posteriori (MAP) estimator combined with a Wiener filter. A pre-processing stage was also included, to correct or eliminate differences in the image intensities and to transform the low-resolution images to a common spatial reference system. The MAP estimation includes an observation image model that represents the different contributions to the voxel intensities based on a 3D Gaussian function. A quantitative and qualitative assessment was performed using synthetic and real images, showing that the proposed approach produces a high-resolution image with significant improvements (about 3dB in PSNR) with respect to a simple trilinear interpolation. The Wiener filter shows little contribution to the final result, demonstrating that the MAP uniformity prior is able to filter out a large amount of the acquisition noise. {\textcopyright} 2013 SPIE. },
}

@inbook{Rusu2013,
  year = { 2013 },
  volume = { 8669 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Statistical 3D prostate imaging atlas construction via anatomically constrained registration },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 866913 },
  issn = { 0277-786X },
  isbn = { 9780819494436 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.2006941 },
  booktitle = { Medical Imaging 2013: Image Processing },
  author = { Rusu and Bloch and Jaffe and Rofsky and Genega and Feleppa and Lenkinski and Madabhushi },
  address = { Bellingham },
  abstract = { Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the identification and validation of meaningful imaging signatures for disease characterization in vivo within a population. Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed. In this work we introduce a novel framework for MRI atlas construction that uses an iterative, anatomically constrained registration (AnCoR) scheme to enable the proper alignment of the prostate (Pr) and central gland (CG) boundaries. Our current implementation uses endorectal, 1.5T or 3T, T2-weighted MRI from 51 patients with biopsy confirmed cancer; however, the prostate atlas is seamlessly extensible to include additional MRI parameters. In our cohort, radical prostatectomy is performed following MP-MR image acquisition; thus ground truth annotations for prostate cancer are available from the histological specimens. Once mapped onto MP-MRI through elastic registration of histological slices to corresponding T2-w MRI slices, the annotations are utilized by the AnCoR framework to characterize the 3D statistical distribution of cancer per anatomic structure. Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo. We evaluate our approach via the Dice similarity coefficient (DSC) for different anatomic structures (delineated by expert radiologists): Pr, CG and peripheral zone (PZ). The AnCoR-based atlas had a CG DSC of 90.36{\%}, and Pr DSC of 89.37{\%}. Moreover, we evaluated the deviation of anatomic landmarks, the urethra and veromontanum, and found 3.64 mm and respectively 4.31 mm. Alternative strategies that use only the T2-w MRI or the prostate surface to drive the registration were implemented as comparative approaches. The AnCoR framework outperformed the alternative strategies by providing the lowest landmark deviations. {\textcopyright} 2013 SPIE. },
}

@article{Sobottka2013,
  year = { 2013 },
  volume = { 119 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881515898{\&}doi=10.3171{\%}2F2013.5.JNS122155{\&}partnerID=40{\&}md5=8ed4109f65f8364384e1830b43ef2107 },
  type = { Journal Article },
  title = { Intraoperative optical imaging of intrinsic signals: A reliable method for visualizing stimulated functional brain areas during surgery },
  pages = { 853--863 },
  number = { 4 },
  keywords = { Brain tumors,Functional brain mapping,Functional imaging,Functional neurosurgery,Image-guided surgery,Intraoperative optical imaging,Intrinsic signals,Oncology,Somatosensory cortex },
  journal = { Journal of Neurosurgery },
  issn = { 00223085 },
  doi = { 10.3171/2013.5.JNS122155 },
  author = { Sobottka and Meyer and Kirsch and Koch and Steinmeier and Morgenstern and Schackert },
  abstract = { Object. Intraoperative optical imaging (IOI) is an experimental technique used for visualizing functional brain areas after surgical exposure of the cerebral cortex. This technique identifies areas of local changes in blood volume and oxygenation caused by stimulation of specific brain functions. The authors describe a new IOI method, including innovative data analysis, that can facilitate intraoperative functional imaging on a routine basis. To evaluate the reliability and validity of this approach, they used the new IOI method to demonstrate visualization of the median nerve area of the somatosensory cortex. Methods. In 41 patients with tumor lesions adjacent to the postcentral gyrus, lesions were surgically removed by using IOI during stimulation of the contralateral median nerve. Optical properties of the cortical tissue were measured with a sensitive camera system connected to a surgical microscope. Imaging was performed by using 9 cycles of alternating prolonged stimulation and rest periods of 30 seconds. Intraoperative optical imaging was based on blood volume changes detected by using a filter at an isosbestic wavelength ($\lambda$ = 568 nm). A spectral analysis algorithm was used to improve computation of the activity maps. Movement artifacts were compensated for by an elastic registration algorithm. For validation, intraoperative conduction of the phase reversal over the central sulcus and postoperative evaluation of the craniotomy site were used. Results. The new method and analysis enabled significant differentiation (p {\textless} 0.005) between functional and nonfunctional tissue. The identification and visualization of functionally intact somatosensory cortex was highly reliable; sensitivity was 94.4{\%} and specificity was almost 100{\%}. The surgeon was provided with a 2D high-resolution activity map within 12 minutes. No method-related side effects occurred in any of the 41 patients. Conclusions. The authors' new approach makes IOI a contact-free and label-free optical technique that can be used safely in a routine clinical setup. Intraoperative optical imaging can be used as an alternative to other methods for the identification of sensory cortex areas and offers the added benefit of a high-resolution map of functional activity. It has great potential for visualizing and monitoring additional specific functional brain areas such as the visual, motor, and speech cortex. A prospective national multicenter clinical trial is currently being planned. {\textcopyright} AANS, 2013. },
}

@article{Bieri2013,
  year = { 2013 },
  volume = { 70 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883229895{\&}doi=10.1002{\%}2Fmrm.24858{\&}partnerID=40{\&}md5=8542d776ecb40faea69bd7bf6e63ebf1 },
  type = { Journal Article },
  title = { Ultra-fast steady state free precession and its application to in vivo 1H morphological and functional lung imaging at 1.5 tesla },
  pages = { 657--663 },
  number = { 3 },
  keywords = { SSFP,imaging,lung,steady state,ultra-fast },
  journal = { Magnetic Resonance in Medicine },
  issn = { 07403194 },
  doi = { 10.1002/mrm.24858 },
  author = { Bieri },
  abstract = { Purpose The speed limit for three-dimensional Fourier-encoded steady state free precession (SSFP) imaging is explored on a clinical whole body system and pushed toward a pulse repetition time (TR) close to or even below the 1 ms regime; in the following referred to as ultra-fast SSFP imaging. Methods To this end, contemporary optimization strategies, such as efficient gradient switching patterns, partial echoes, ramp sampling techniques, and a target-related design of excitation pulses were applied to explore the lower boundaries in TR for SSFP-based Cartesian imaging. Results Generally, minimal TR was limited in vivo by peripheral nerve stimulation, allowing a TR ∼1 ms for isotropic resolutions down to about 2 mm. As a result, ultra-fast balanced SSFP provides artifact-free images even for targets with severe susceptibility variations, and native high-resolution structural and functional in vivo 1H imaging of the human lung is demonstrated at 1.5 T. Conclusion On clinical whole body MRI systems, the TR of SSFP-based Cartesian imaging can be pushed toward the 1 ms regime. As a result, ultra-fast SSFP protocols might represent a promising new powerful approach for SSFP-based imaging, not only for lung but also in a variety of clinical and scientific applications. Copyright {\textcopyright} 2013 Wiley Periodicals, Inc. },
}

@article{Wollny2013,
  year = { 2013 },
  volume = { 8 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885357120{\&}doi=10.1186{\%}2F1751-0473-8-20{\&}partnerID=40{\&}md5=67c49f93d1bf780334ae506f97a71ff2 },
  type = { Journal Article },
  title = { MIA - A free and open source software for gray scale medical image analysis },
  journal = { Source Code for Biology and Medicine },
  issn = { 17510473 },
  doi = { 10.1186/1751-0473-8-20 },
  author = { Wollny and Kellman and Ledesma-Carbayo and Skinner and Hublin and Hierl },
  abstract = { Background: Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don't provide an clear approach when one wants to shape a new command line tool from a prototype shell script.Results: The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code.Conclusion: In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed. {\textcopyright} 2013 Wollny et al.; licensee BioMed Central Ltd. },
}

@article{Urish2013,
  year = { 2013 },
  volume = { 4 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Registration of Magnetic Resonance Image Series for Knee Articular Cartilage Analysis: Data from the Osteoarthritis Initiative },
  pages = { 20--27 },
  number = { 1 },
  keywords = { DESS,MRI,T2,cartilage,registration },
  journal = { Cartilage },
  issn = { 19476035 },
  doi = { 10.1177/1947603512451745 },
  author = { Urish and Williams and Durkin and Chu },
  abstract = { Objective: Although conventional radiography is used to assess osteoarthritis in a clinical setting, it has limitations, including an inability to stage early cartilage degeneration. There is a growing interest in using quantitative magnetic resonance imaging to identify degenerative changes in articular cartilage, including the large multicentered study, the Osteoarthritis Initiative (OAI). There is a demand for suitable image registration and segmentation software to complete this analysis. The objective of this study was to develop and validate the open source software, ImageK, that registers 3 T MRI T2 mapping and double echo steady state (DESS) knee MRI sequences acquired in the OAI protocol. Methods: A C++ library, the insight toolkit, was used to develop open source software to register DESS and T2 mapping image MRI sequences using Mattes's Multimodality Mutual information metric. Results: Registration was assessed using three separate methods. A checkerboard layout demonstrated acceptable visual alignment. Fiducial markers placed in cadaveric knees measured a registration error of 0.85 voxels. Measuring the local variation in Mattes's Mutual Information metric in the local area of the registered solution showed precision within 1 pixel. In this group, the registered solution required a transform of 56 voxels in translation and 1 degree of rotation. Conclusion: The software we have developed, ImageK, provides free, open source image analysis software that registers DESS and T2 mapping sequences of knee articular cartilage within 1 voxel accuracy. This image registration software facilitates quantitative MRI analyses of knee articular cartilage. {\textcopyright} The Author(s) 2013. },
}

@article{Sjoberg2013,
  year = { 2013 },
  volume = { 110 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures },
  pages = { 308--319 },
  number = { 3 },
  keywords = { Atlas based segmentation,Deformable registration,Label fusion,Multi-atlas segmentation,Radiotherapy prostate,Segmentation },
  journal = { Computer Methods and Programs in Biomedicine },
  issn = { 01692607 },
  doi = { 10.1016/j.cmpb.2012.12.006 },
  author = { Sj{\"{o}}berg and Ahnesj{\"{o}} },
  abstract = { Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure. {\textcopyright} 2013 Elsevier Ireland Ltd. },
}

@article{Oelschlagel2013,
  year = { 2013 },
  volume = { 58 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Evaluation of intraoperative optical imaging analysis methods by phantom and patient measurements },
  pages = { 257--267 },
  number = { 3 },
  keywords = { Data analysis,Intrinsic signals,Optical imaging,Spectral analysis },
  journal = { Biomedizinische Technik },
  issn = { 00135585 },
  doi = { 10.1515/bmt-2012-0077 },
  author = { Oelschlagel and Meyer and Wahl and Sobottka and Kirsch and Schackert and Morgenstern },
  abstract = { Intraoperative optical imaging (IOI) is a localization method for functional areas of the human brain cortex during neurosurgical procedures. The aim of the current work was to develop of a new analysis technique for the computation of two-dimensional IOI activity maps that is suited especially for use in clinical routine. The new analysis technique includes a stimulation scheme that comprises 30-s rest and 30-s stimulation conditions, in connection with pixelwise spectral power analysis for activity map calculation. A software phantom was used for verification of the implemented algorithms as well as for the comparison with the commonly used relative difference imaging method. Furthermore, the analysis technique was tested using intraoperative measurements on eight patients. The comparison with the relative difference algorithm revealed an averaged improvement of the signal-to-noise ratio between 95{\%} and 130{\%} for activity maps computed from intraoperatively acquired patient datasets. The results show that the new imaging technique improves the activity map quality of IOI especially under difficult intraoperative imaging conditions and is therefore especially suited for use in clinical routine. {\textcopyright} 2013 Walter de Gruyter GmbH. },
}

@article{Meyer2013a,
  year = { 2013 },
  volume = { 58 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Intraoperative optical imaging of functional brain areas for improved image-guided surgery },
  pages = { 225--236 },
  number = { 3 },
  keywords = { Functional imaging,Intrinsic signals,Somatosensory cortex },
  journal = { Biomedizinische Technik },
  issn = { 00135585 },
  doi = { 10.1515/bmt-2012-0072 },
  author = { Meyer and Sobottka and Kirsch and Schackert and Steinmeier and Koch and Morgenstern },
  abstract = { Intraoperative optical imaging of intrinsic signals can improve the localization of functional areas of the cortex. On the basis of a review of the current state of technology, a setup was developed and evaluated. The aim was to implement an easy-to-use and robust imaging setup that can be used in clinical routine with standard hardware equipment (surgical microscope, high-resolution camera, stimulator for peripheral nerve stimulation) and custom-made software for intraoperative and postoperative data analysis. Evaluation of different light sources (halogen, xenon) showed a sufficient temporal behavior of xenon light without using a stabilized power supply. Spatial binning (2 ×?2) of the camera reduces temporal variations in the images by preserving a high spatial resolution. The setup was tested in eight patients. Images were acquired continuously for 9 min with alternating 30-s rest and 30-s stimulation conditions. Intraoperative measurement and visualization of high-resolution two-dimensional activity maps could be achieved in 15 min. The detected functional regions corresponded with anatomical and electrophysiological validation. The integration of optical imaging in clinical routine could successfully be achieved using standard hardware, which improves guidance for the surgeon during interventions near the eloquent areas of the brain. {\textcopyright} 2013 Walter de Gruyter GmbH. },
}

@article{McCormick2013,
  year = { 2013 },
  volume = { 35 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { An approach to unbiased subsample interpolation for motion tracking },
  pages = { 76--89 },
  number = { 2 },
  keywords = { motion tracking,sinc reconstruction,strain imaging,subsample interpolation },
  journal = { Ultrasonic Imaging },
  issn = { 01617346 },
  doi = { 10.1177/0161734613476176 },
  author = { McCormick and Varghese },
  abstract = { Accurate subsample displacement estimation is necessary for ultrasound elastography because of the small deformations that occur and the subsequent application of a derivative operation on local displacements. Many of the commonly used subsample estimation techniques introduce significant bias errors. This article addresses a reduced bias approach to subsample displacement estimations that consists of a two-dimensional windowed-sinc interpolation with numerical optimization. It is shown that a Welch or Lanczos window with a Nelder-Mead simplex or regular-step gradient-descent optimization is well suited for this purpose. Little improvement results from a sinc window radius greater than four data samples. The strain signal-to-noise ratio (SNR) obtained in a uniformly elastic phantom is compared with other parabolic and cosine interpolation methods; it is found that the strain SNR ratio is improved over parabolic interpolation from 11.0 to 13.6 in the axial direction and 0.7 to 1.1 in the lateral direction for an applied 1{\%} axial deformation. The improvement was most significant for small strains and displacement tracking in the lateral direction. This approach does not rely on special properties of the image or similarity function, which is demonstrated by its effectiveness with the application of a previously described regularization technique. {\textcopyright} The Author(s) 2013. },
}

@article{LaTorre2013,
  year = { 2013 },
  volume = { 7 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { 3D segmentations of neuronal nuclei from confocal microscope image stacks },
  pages = { 10 },
  number = { DEC },
  keywords = { 3D reconstruction,Automatic segmentation,Cerebral cortex,Image processing,Neuron },
  journal = { Frontiers in Neuroanatomy },
  issn = { 16625129 },
  doi = { 10.3389/fnana.2013.00049 },
  author = { Latorre and Alonso-Nanclares and Muelas and Pe{\~{n}}a and Defelipe },
  abstract = { In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario-the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei. {\textcopyright} 2013 LaTorre, Alonso-Nanclares, Muelas, Pe{\~{n}}a and DeFelipe. },
}

@article{Kremer2013,
  year = { 2013 },
  volume = { 39 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883465702{\&}doi=10.1016{\%}2Fj.ultrasmedbio.2013.04.004{\&}partnerID=40{\&}md5=7e4e566e8788b13da38aa8c29cc1271b },
  type = { Journal Article },
  title = { 2-D Strain Assessment in the Mouse Through Spatial Compounding of Myocardial Velocity Data: InVivo Feasibility },
  pages = { 1848--1860 },
  number = { 10 },
  keywords = { 2-D myocardial strain,Doppler,Mouse,Spatial compounding },
  journal = { Ultrasound in Medicine and Biology },
  issn = { 1879291X },
  doi = { 10.1016/j.ultrasmedbio.2013.04.004 },
  author = { Kremer and Dresselaers and Heyde and Ferferieva and Caluw{\'{e}} and Choi and Claus and Oosterlinck and Janssens and Himmelreich and D'hooge },
  abstract = { Ultrasound assessment of myocardial strain can provide valuable information on regional cardiac function. However, Doppler-based methods often used in practice for strain estimation suffer from angle dependency. In this study, a partial solution to that fundamental limitation is presented. We have previously reported using simulated data sets that spatial compounding of axial velocities obtained at three steering angles can theoretically outperform 2-D speckle tracking for 2-D strain estimation in the mouse heart. In this study, the feasibility of the method was analyzed invivo using spatial compounding of Doppler velocities on six mice with myocardial infarction and five controls, and results were compared with those of tagged microscopic magnetic resonance imaging ($\mu$MRI). Circumferential estimates quantified by means of both ultrasound and $\mu$MRI could detect regional dysfunction. Between echocardiography and $\mu$MRI, a good regression coefficient was obtained for circumferential strain estimates (. r= 0.69), whereas radial strain estimates correlated only moderately (. r= 0.37). A second echocardiography was performed after $\mu$MRI to test the reproducibility of the compounding method. This yielded a higher correlation coefficient for the circumferential component than for the radial component (. r= 0.74 circumferentially, r= 0.49 radially). {\textcopyright} 2013 World Federation for Ultrasound in Medicine {\&} Biology. },
}

@article{Heyde2013,
  year = { 2013 },
  volume = { 39 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Elastic image registration to quantify 3-d regional myocardial deformation from volumetric ultrasound: Experimental validation in an animal model },
  pages = { 1688--1697 },
  number = { 9 },
  keywords = { Echocardiography,Elastic registration,Invivo,Sonomicrometry,Strain,Validation },
  journal = { Ultrasound in Medicine and Biology },
  issn = { 1879291X },
  doi = { 10.1016/j.ultrasmedbio.2013.02.463 },
  author = { Heyde and Bouchez and Thieren and Vandenheuvel and Jasaityte and Barbosa and Claus and Maes and Wouters and D'hooge },
  abstract = { Although real-time 3-D echocardiography has the potential to allow more accurate assessment of global and regional ventricular dynamics compared with more traditional 2-D ultrasound examinations, it still requires rigorous testing and validation should it break through as a standard examination in routine clinical practice. However, only a limited number of studies have validated 3-D strain algorithms in an invivo experimental setting. The aim of the present study, therefore, was to validate a registration-based strain estimation methodology in an animal model. Volumetric images were acquired in 14 open-chest sheep instrumented with ultrasonic microcrystals. Radial strain (e{\{}open{\}}RR), longitudinal strain (e{\{}open{\}}LL) and circumferential strain (e{\{}open{\}}CC) were estimated during different stages: at rest, during reduced and increased cardiac inotropy induced by esmolol and dobutamine infusion, respectively, and during acute ischemia. Agreement between image-based and microcrystal-based strain estimates was evaluated by their linear correlation, indicating that all strain components could be estimated with acceptable accuracy (r= 0.69 for e{\{}open{\}}RR, r= 0.64 for e{\{}open{\}}LL and r= 0.62 for e{\{}open{\}}CC). These findings are comparable to the performance of the current state-of-the-art commercial 3-D speckle tracking methods. Furthermore, shape of the strain curves, timing of peak values and location of dysfunctional regions were identified well. Whether 3-D elastic registration performs better than 3-D block matching-based methodologies still remains to be proven. {\textcopyright} 2013 World Federation for Ultrasound in Medicine {\&} Biology. },
}

@article{Garpebring2013,
  year = { 2013 },
  volume = { 69 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Uncertainty estimation in dynamic contrast-enhanced MRI },
  pages = { 992--1002 },
  number = { 4 },
  keywords = { accuracy,dynamic contrast-enhanced-MRI,precision analysis,uncertainty estimation },
  journal = { Magnetic Resonance in Medicine },
  issn = { 07403194 },
  doi = { 10.1002/mrm.24328 },
  author = { Garpebring and Brynolfsson and Yu and Wirestam and Johansson and Asklund and Karlsson },
  abstract = { Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15{\%} in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15{\%} errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23{\%}, 32{\%}, and 29{\%} for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty. {\textcopyright} 2012 Wiley Periodicals, Inc. },
}

@article{Bui2013,
  year = { 2013 },
  volume = { 20 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Imaging-based observational databases for clinical problem solving: The role of informatics },
  pages = { 1053--1058 },
  number = { 6 },
  journal = { Journal of the American Medical Informatics Association },
  issn = { 10675027 },
  doi = { 10.1136/amiajnl-2012-001340 },
  author = { Bui and Hsu and Arnold and El-Saden and Aberle and Taira },
  abstract = { Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving-from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care. },
}

@article{Akbarzadeh2013,
  year = { 2013 },
  volume = { 27 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { MRI-guided attenuation correction in whole-body PET/MR: Assessment of the effect of bone attenuation },
  pages = { 152--162 },
  number = { 2 },
  keywords = { Attenuation correction,PET/CT,PET/MRI,Quantification,Tissue classification },
  journal = { Annals of Nuclear Medicine },
  issn = { 09147187 },
  doi = { 10.1007/s12149-012-0667-3 },
  author = { Akbarzadeh and Ay and Ahmadian and {Riahi Alam} and Zaidi },
  abstract = { Objective: Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. Methods: This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. Results: The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8 {\%} in the body and up to 31.0 {\%} for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15 {\%} for lesions located in the body and 30.7 {\%} for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30 {\%} of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. Conclusion: Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies. {\textcopyright} 2012 The Japanese Society of Nuclear Medicine. },
}

@incollection{Seidel2013a,
  year = { 2013 },
  volume = { 7945 LNCS },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879835400{\&}doi=10.1007{\%}2F978-3-642-38899-6{\_}36{\&}partnerID=40{\&}md5=f7e591d9366417855cbf78f95bd7e1d2 {\%}3CGo to },
  type = { Serial },
  title = { A semi-automatic approach for segmentation of three-dimensional microscopic image stacks of cardiac tissue },
  series = { Lecture Notes in Computer Science },
  publisher = { Springer-Verlag Berlin },
  pages = { 300--307 },
  keywords = { algorithm,cardiac tissue,confocal microscopy,segmentation,three-dimensional },
  issn = { 03029743 },
  isbn = { 9783642388989 },
  editor = { [object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-642-38899-6_36 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Seidel and Draebing and Seemann and Sachse },
  address = { Berlin },
  abstract = { The segmentation of three-dimensional microscopic images of cardiac tissues provides important parameters for characterizing cardiac diseases and modeling of tissue function. Segmenting these images is, however, challenging. Currently only time-consuming manual approaches have been developed for this purpose. Here, we introduce an efficient approach for the semi-automatic segmentation (SAS) of cardiomyocytes and the extracellular space in image stacks obtained from confocal microscopy. The approach is based on a morphological watershed algorithm and iterative creation of watershed seed points on a distance map. Results of SAS were consistent with results from manual segmentation (Dice similarity coefficient: 90.8±2.6{\%}). Cell volume was 4.6±6.5{\%} higher in SAS cells, which mainly resulted from cell branches and membrane protrusions neglected by manual segmentation. We suggest that the novel approach constitutes an important tool for characterizing normal and diseased cardiac tissues. Furthermore, the approach is capable of providing crucial parameters for modeling of tissue structure and function. {\textcopyright} 2013 Springer-Verlag. },
}

@inproceedings{Kugu2013,
  year = { 2013 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883888988{\&}doi=10.1109{\%}2FRAST.2013.6581204{\&}partnerID=40{\&}md5=eba34fda9e6d5ba7ef3f98fd189e8ed5 {\%}3CGo to },
  type = { Book },
  title = { Satellite image denoising using Bilateral Filter with SPEA2 optimized parameters },
  series = { Proceedings of 6th International Conference on Recent Advances in Space Technologies },
  publisher = { Ieee },
  pages = { 217--223 },
  keywords = { Image denoising,SPEA2,bilateral filter,parameter optimization },
  isbn = { 9781467363938 },
  doi = { 10.1109/RAST.2013.6581204 },
  booktitle = { RAST 2013 - Proceedings of 6th International Conference on Recent Advances in Space Technologies },
  author = { Kugu },
  address = { New York },
  abstract = { Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of these algorithms accept several parameters from the user to reach the best results. In the process of denoising, there is always a competition between the noise reduction and the fine preservation. If there is a competition between the objectives then an evolutionary multi objective optimization (EMO) is needed. In this work, the parameters of the image denoising algorithms have been optimized to minimize the trade-off by using improved Strength Pareto Evolutionary Algorithm (SPEA2). SPEA2 differs from the other EMO algorithms with the fitness assignment, the density estimation and the archive truncation processes. There is no single optimal solution in a multi objective problems instead there is a set of solutions called as Pareto efficient. Four objective functions, namely Mean Square Error (MSE), Entropy, Structural SIMilarity (SSIM) and Second Derivative of the image, have been used in this work. MSE is calculated by taking the square of difference between the noise free image and the deniosed image. Entropy is a measure of randomness of the content of difference image. The lower entropy is the better. The second derivate of an image can be achieved by convolving the image with the Laplacian Mask. SSIM algorithm is based on the similarities of the structures on the noise free image and the structures of the denoised image. For the image enhancement algorithms, Insight Segmentation and Registration Toolkit (ITK) is selected. ITK is an open source project and it is being developed in C++ to provide developers with a rich set of applications for image analysis. It includes tens of image filters for the registration and segmentation purposes. In this work, Bilateral Image Filter is evaluated in the field of satellite imaging for the noise removal process. The evaluated filter receives two parameters from the user side within their predefined ranges. Here, SPEA2 algorithm takes the responsibility to optimize these parameters to reach the best noise free image results. SPEA2 algorithm was implemented in Matlab and executable files of image filter were called in Matlab environment. The results of the work were represented graphically to show the effectiveness of selected method. {\textcopyright} 2013 IEEE. },
}

@inbook{Haak2012a,
  year = { 2012 },
  volume = { 8320 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Comparison of spatiotemporal interpolators for 4D image reconstruction from 2D transesophageal ultrasound },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 832007 },
  issn = { 16057422 },
  isbn = { 9780819489692 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.905893 },
  booktitle = { Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy },
  author = { Haak and Stralen and Burken and Klein and Pluim and Jong and Steen and Bosch },
  address = { Bellingham },
}

@article{Ziegeler2012,
  year = { 2012 },
  volume = { 29 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Spatial error metrics for oceanographic model verification },
  pages = { 260--266 },
  number = { 2 },
  keywords = { Error analysis,Forecast verification,Ocean models },
  journal = { Journal of Atmospheric and Oceanic Technology },
  issn = { 07390572 },
  doi = { 10.1175/JTECH-D-11-00109.1 },
  author = { Ziegeler and Dykes and Shriver },
  abstract = { A common problem with modern numerical oceanographic models is spatial displacement, including misplacement and misshapenness of ocean circulation features. Traditional error metrics, such as least squares methods, are ineffective in many such cases; for example, only small errors in the location of a frontal pattern are translated to large differences in least squares of intensities. Such problems are common in meteorological forecast verification as well, so the application of spatial error metrics have been a recently popular topic there. Spatial error metrics separate model error into a displacement component and an intensity component, providing a more reliable assessment of model biases and a more descriptive portrayal of numerical model prediction skill. The application of spatial error metrics to oceanographic models has been sparse, and further advances for both meteorology and oceanography exist in the medical imaging field. These advances are presented, along with modifications necessary for oceanographic model output. Standard methods and options for those methods in the literature are explored, and where the best arrangements of options are unclear, comparison studies are conducted. These trials require the reproduction of synthetic displacements in conjunction with synthetic intensity perturbations across 480 Navy Coastal Ocean Model (NCOM) temperature fields from various regions of the globe throughout 2009. Study results revealed the success of certain approaches novel to both meteorology and oceanography, including B-spline transforms and mutual information. That, combined with other common methods, such as quasi-Newton optimization and land masking, could best recover the synthetic displacements under various synthetic intensity changes. {\textcopyright} 2012 American Meteorological Society. },
}

@article{Wu2012,
  year = { 2012 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A line profile-based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment },
  pages = { 20--31 },
  number = { 2 },
  keywords = { Double scanning,Oversized patients,Partial registration,Planning CT },
  journal = { Journal of Applied Clinical Medical Physics },
  issn = { 15269914 },
  doi = { 10.1120/jacmp.v13i2.3629 },
  author = { Wu and Zhao and Cao and Das },
  abstract = { True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation {\&} Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning. },
}

@article{Pietzsch2012,
  year = { 2012 },
  volume = { 28 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Img lib 2-generic image processing in Java },
  pages = { 3009--3011 },
  number = { 22 },
  journal = { Bioinformatics },
  issn = { 14602059 },
  doi = { 10.1093/bioinformatics/bts543 },
  author = { Pietzsch and Preibisch and Toman{\v{c}}{\'{a}}k and Saalfeld },
  abstract = { ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixelalgebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. {\textcopyright} 2012 The Author. },
}

@article{Mendoza2012,
  year = { 2012 },
  volume = { 23 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Fast parameter-free region growing segmentation with application to surgical planning },
  pages = { 165--177 },
  number = { 1 },
  keywords = { CT,Region growing,Segmentation,Surgical planning,Virtual reality },
  journal = { Machine Vision and Applications },
  issn = { 09328092 },
  doi = { 10.1007/s00138-010-0274-z },
  author = { Mendoza and Acha and Serrano and G{\'{o}}mez-C{\'{i}}a },
  abstract = { In this paper, we propose a self-assessed adaptive region growing segmentation algorithm. In the context of an experimental virtual-reality surgical planning software platform, our method successfully delineates main tissues relevant for reconstructive surgery, such as fat, muscle, and bone. We rely on a self-tuning approach to deal with a great variety of imaging conditions requiring limited user intervention (one seed). The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region, and the stopping criterion is adapted to the noise level in the dataset thanks to the sampling strategy used for the assessment function. Sampling is referred to the statistics of a neighborhood around the seed(s), so that the sampling period becomes greater when images are noisier, resulting in the acquisition of a lower frequency version of the contrast function. Validation is provided for synthetic images, as well as real CT datasets. For the CT test images, validation is referred to manual delineations for 10 cases and to subjective assessment for another 35. High values of sensitivity and specificity, as well as Dice's coefficient and Jaccard's index on one hand, and satisfactory subjective evaluation on the other hand, prove the robustness of our contrast-based measure, even suggesting suitability for calibration of other region-based segmentation algorithms. {\textcopyright} 2010 Springer-Verlag. },
}

@article{Kortesniemi2012,
  year = { 2012 },
  volume = { 53 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Organ dose calculation in CT based on scout image data and automatic image registration },
  pages = { 908--913 },
  number = { 8 },
  keywords = { Computed tomography,Dosimetry,Technical aspects },
  journal = { Acta Radiologica },
  issn = { 02841851 },
  doi = { 10.1258/ar.2012.110611 },
  author = { Kortesniemi and Salli and Seuri },
  abstract = { Background: Computed tomography (CT) has become the main contributor of the cumulative radiation exposure in radiology. Information on cumulative exposure history of the patient should be available for efficient management of radiation exposures and for radiological justification. Purpose: To develop and evaluate automatic image registration for organ dose calculation in CT. Material and Methods: Planning radiograph (scout) image data describing CT scan ranges from 15 thoracic CT examinations (9 men and 6 women) and 10 abdominal CT examinations (6 men and 4 women) were co-registered with the reference trunk CT scout image. 2-D affine transformation and normalized correlation metric was used for image registration. Longitudinal (z-axis) scan range coordinates on the reference scout image were converted into slice locations on the CT-Expo anthropomorphic male and female models, following organ and effective dose calculations. Results: The average deviation of z-location of studied patient images from the corresponding location in the reference scout image was 6.2 mm. The ranges of organ and effective doses with constant exposure parameters were from 0 to 28.0 mGy and from 7.3 to 14.5 mSv, respectively. The mean deviation of the doses for fully irradiated organs (inside the scan range), partially irradiated organs and non-irradiated organs (outside the scan range) was 1{\%}, 5{\%}, and 22{\%}, respectively, due to image registration. Conclusion: The automated image processing method to registrate individual chest and abdominal CT scout radiograph with the reference scout radiograph is feasible. It can be used to determine the individual scan range coordinates in z-direction to calculate the organ dose values. The presented method could be utilized in automatic organ dose calculation in CT for radiation exposure tracking of the patients. },
}

@article{Kankaanpaa2012,
  year = { 2012 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { BioImageXD: An open, general-purpose and high-throughput image-processing platform },
  pages = { 683--689 },
  number = { 7 },
  journal = { Nature Methods },
  issn = { 15487091 },
  doi = { 10.1038/nmeth.2047 },
  author = { Kankaanp{\"{a}}{\"{a}} and Paavolainen and Tiitta and Karjalainen and P{\"{a}}iv{\"{a}}rinne and Nieminen and Marjom{\"{a}}ki and Heino and White },
  abstract = { BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors. {\textcopyright} 2012 Nature America, Inc. All rights reserved. },
}

@article{Busch2012,
  year = { 2012 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A microfluidic device and computational platform for high-throughput live imaging of gene expression },
  pages = { 1101--1106 },
  number = { 11 },
  journal = { Nature Methods },
  issn = { 15487091 },
  doi = { 10.1038/nmeth.2185 },
  author = { Busch and Moore and Martsberger and MacE and Twigg and Jung and Pruteanu-Malinici and Kennedy and Fricke and Clark and Ohler and Benfey },
  abstract = { To fully describe gene expression dynamics requires the ability to quantitatively capture expression in individual cells over time. Automated systems for acquiring and analyzing real-time images are needed to obtain unbiased data across many samples and conditions. We developed a microfluidics device, the RootArray, in which 64 Arabidopsis thaliana seedlings can be grown and their roots imaged by confocal microscopy over several days without manual intervention. To achieve high throughput, we decoupled acquisition from analysis. In the acquisition phase, we obtain images at low resolution and segment to identify regions of interest. Coordinates are communicated to the microscope to record the regions of interest at high resolution. In the analysis phase, we reconstruct three-dimensional objects from stitched high-resolution images and extract quantitative measurements from a virtual medial section of the root. We tracked hundreds of roots to capture detailed expression patterns of 12 transgenic reporter lines under different conditions. {\textcopyright} 2012 Nature America, Inc. All rights reserved. },
}

@article{Beriault2012,
  year = { 2012 },
  volume = { 7 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { A multi-modal approach to computer-assisted deep brain stimulation trajectory planning },
  pages = { 687--704 },
  number = { 5 },
  keywords = { Decision-support system,Deep brain stimulation,Image-guided neurosurgery,Parkinson's disease,Preoperative planning },
  journal = { International Journal of Computer Assisted Radiology and Surgery },
  issn = { 18616429 },
  doi = { 10.1007/s11548-012-0768-4 },
  author = { B{\'{e}}riault and Subaie and Collins and Sadikot and Pike },
  abstract = { Purpose: Both frame-based and frameless approaches to deep brain stimulation (DBS) require planning of insertion trajectories that mitigate hemorrhagic risk and loss of neurological function. Currently, this is done by manual inspection of multiple potential electrode trajectories on MR-imaging data. We propose and validate a method for computer-assisted DBS trajectory planning. Method: Our framework integrates multi-modal MRI analysis (T1w, SWI, TOF-MRA) to compute suitable DBS trajectories that optimize the avoidance of specific critical brain structures. A cylinder model is used to process each trajectory and to evaluate complex surgical constraints described via a combination of binary and fuzzy segmented datasets. The framework automatically aggregates the multiple constraints into a unique ranking of recommended low-risk trajectories. Candidate trajectories are represented as a few well-defined cortical entry patches of best-ranked trajectories and presented to the neurosurgeon for final trajectory selection. Results: The proposed algorithm permits a search space containing over 8,000 possible trajectories to be processed in less than 20 s. A retrospective analysis on 14 DBS cases of patients with severe Parkinson's disease reveals that our framework can improve the simultaneous optimization of many pre-formulated surgical constraints. Furthermore, all automatically computed trajectories were evaluated by two neurosurgeons, were judged suitable for surgery and, in many cases, were judged preferable or equivalent to the manually planned trajectories used during the operation. Conclusions: This work provides neurosurgeons with an intuitive and flexible decision-support system that allows objective and patient-specific optimization of DBS lead trajectories, which should improve insertion safety and reduce surgical time. {\textcopyright} 2012 CARS. },
}

@article{Allan2012,
  year = { 2012 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { OMERO: Flexible, model-driven data management for experimental biology },
  pages = { 245--253 },
  number = { 3 },
  journal = { Nature Methods },
  issn = { 15487091 },
  doi = { 10.1038/nmeth.1896 },
  author = { Allan and Burel and Moore and Blackburn and Linkert and Loynton and MacDonald and Moore and Neves and Patterson and Porter and Tarkowska and Loranger and Avondo and Lagerstedt and Lianas and Leo and Hands and Hay and Patwardhan and Best and Kleywegt and Zanetti and Swedlow },
  abstract = { Data-intensive research depends on tools that manage multidimensional, heterogeneous datasets. We built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices and tables. OMERO's design and flexibility have enabled its use for light-microscopy, high-content-screening, electron-microscopy and even non-image-genotype data. OMERO is open-source software, available at http://openmicroscopy.org/. {\textcopyright} 2012 Nature America, Inc. All rights reserved. },
}

@incollection{Avants2012,
  year = { 2012 },
  volume = { 7359 LNCS },
  url = { http://link.springer.com/10.1007/978-3-642-31340-0{\_}28 http://link.springer.com/chapter/10.1007{\%}2F978-3-642-31340-0{\_}28 http://link.springer.com/chapter/10.1007{\%}252F978-3-642-31340-0{\_}28 },
  title = { A unified image registration framework for ITK },
  publisher = { Springer Berlin Heidelberg },
  pages = { 266--275 },
  organization = { Springer Berlin Heidelberg },
  number = { LNCS 7359 },
  issn = { 03029743 },
  isbn = { 9783642313394 },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Avants et al/Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)/Avants et al. - 2012 - A unified image registration framework for ITK.pdf:pdf },
  editor = { [object Object] },
  edition = { 5th Intern },
  doi = { 10.1007/978-3-642-31340-0_28 },
  chapter = { A Unified },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Avants and Tustison and Song and Wu and Stauffer and McCormick and Johnson and Gee },
  annote = { From Duplicate 2 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

From Duplicate 1 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

From Duplicate 1 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

{\#}{\#}CONTRIBUTIONS: As a member of ITK development team, I worked closely to develop, test, and implement the registration frameworks described in this work.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

From Duplicate 2 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

From Duplicate 1 (A unified image registration framework for ITK - Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Wu, Baohua; Stauffer, Michael; McCormick, Matthew M.; Johnson, Hans J.; Gee, James C.)

{\#}{\#}CONTRIBUTIONS: As a member of ITK development team, I worked closely to develop, test, and implement the registration frameworks described in this work.:{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#} },
  abstract = { Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and may improve reproducibility. Version 4 of the Insight ToolKit ( ITK ) seeks to establish new standards in publicly available image registration methodology. In this work, we provide an overview and preliminary evaluation of the revised toolkit against registration based on the previous major ITK version (3.20). Furthermore, we propose a nomenclature that may be used to discuss registration frameworks via schematic representations. In total, the ITK contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. {\textcopyright} 2012 Springer-Verlag. },
}

@incollection{Kutarnia2012,
  year = { 2012 },
  volume = { 173 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860628403{\&}doi=10.3233{\%}2F978-1-61499-022-2-238{\&}partnerID=40{\&}md5=1799c545e84fbed039abc026f951ab6d },
  type = { Serial },
  title = { Generation of 3D ultrasound training volumes from freehand acquired data },
  pages = { 238--244 },
  keywords = { Freehand ultrasound,Non-rigid registration },
  issn = { 18798365 },
  isbn = { 9781614990215 },
  doi = { 10.3233/978-1-61499-022-2-238 },
  booktitle = { Studies in Health Technology and Informatics },
  author = { Kutarnia and Pedersen },
  abstract = { We are developing a low cost ultrasound training system running on a laptop in which the user scans a generic 3D curved surface representing the patient using a 5 DoF sensor. A critical component of this system is the generation of ultrasound training image volumes, which need to cover a complete body region in order to provide a realistic scanning experience. This research attempts to develop stitching techniques to generate large global volumes from smaller overlapping volumes acquired using freehand techniques. {\textcopyright} 2012 The authors and IOS Press. All rights reserved. },
}

@inproceedings{Chu2012,
  year = { 2012 },
  volume = { 8315 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874884929{\&}doi=10.1117{\%}2F12.911462{\&}partnerID=40{\&}md5=892c7bf1991afd54a3da43cf68fda6d8 },
  type = { Conference Proceedings },
  title = { Automated segmentation of tumors on bone scans using anatomy-specific thresholding },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 83150F },
  issn = { 16057422 },
  isbn = { 9780819489647 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.911462 },
  booktitle = { Medical Imaging 2012: Computer-Aided Diagnosis },
  author = { Chu and Lo and Kim and Lu and Ramakrishna and Gjertson and Poon and Auerbach and Goldin and Brown },
  address = { Bellingham },
  abstract = { Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5{\%}, and specificity of 93.9{\%}. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069. },
}

@article{Pierson2011,
  year = { 2011 },
  volume = { 54 },
  url = { http://www.sciencedirect.com/science/article/pii/S1053811910009055 papers2://publication/uuid/35F9A8B8-DD4A-4857-903F-16D3F7CCAEF7 http://www.ncbi.nlm.nih.gov/pubmed/20600977 },
  title = { Fully automated analysis using BRAINS: AutoWorkup },
  publisher = { Elsevier Inc. },
  pmid = { 20600977 },
  pages = { 328--336 },
  number = { 1 },
  month = { jan },
  keywords = { Automated image analysis,BRAINS,Morphometry,Pipeline,Segmentation,Volumetric analysis },
  journal = { NeuroImage },
  issn = { 10538119 },
  isbn = { 1095-9572 (Electronic)$\backslash$n1053-8119 (Linking) },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Pierson et al/NeuroImage/Pierson et al. - 2011 - Fully automated analysis using BRAINS AutoWorkup.pdf:pdf },
  doi = { 10.1016/j.neuroimage.2010.06.047 },
  author = { Pierson and Johnson and Harris and Keefe and Paulsen and Andreasen and Magnotta },
  annote = { \#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\# },
  abstract = { The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20. years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1{\%}, and manual editing of the brain mask was required on 4{\%} of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions. {\textcopyright} 2010 Elsevier Inc. },
}

@article{RID:1020151228438-28,
  year = { 2011 },
  volume = { 54 },
  url = { http://www.sciencedirect.com/science/article/pii/S1053811910009055 papers2://publication/uuid/35F9A8B8-DD4A-4857-903F-16D3F7CCAEF7 http://www.ncbi.nlm.nih.gov/pubmed/20600977 },
  title = { Fully automated analysis using BRAINS: AutoWorkup },
  publisher = { Elsevier Inc. },
  pmid = { 20600977 },
  pages = { 328--336 },
  number = { 1 },
  month = { jan },
  keywords = { Automated image analysis,BRAINS,Morphometry,Pipeline,Segmentation,Volumetric analysis },
  journal = { NeuroImage },
  issn = { 10538119 },
  isbn = { 1095-9572 (Electronic)$\backslash$n1053-8119 (Linking) },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Pierson et al/NeuroImage/Pierson et al. - 2011 - Fully automated analysis using BRAINS AutoWorkup.pdf:pdf },
  doi = { 10.1016/j.neuroimage.2010.06.047 },
  author = { Pierson and Johnson and Harris and Keefe and Paulsen and Andreasen and Magnotta },
  annote = { From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

{\#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 2 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

{\#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#} },
  abstract = { The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20. years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1{\%}, and manual editing of the brain mask was required on 4{\%} of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions. {\textcopyright} 2010 Elsevier Inc. },
}

@article{Wellein2011,
  year = { 2011 },
  volume = { 26 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251618712{\&}doi=10.1007{\%}2Fs00450-010-0130-4{\&}partnerID=40{\&}md5=63eb5e1f357ccd1387341c04ab3f7522 },
  type = { Journal Article },
  title = { A pipeline for interactive cortex segmentation },
  pages = { 87--96 },
  number = { 1-2 },
  keywords = { Cortex segmentation,Level set method,Neurosurgical intervention planning,Segmentation evaluation,User interaction,Watershed algorithm },
  journal = { Computer Science - Research and Development },
  issn = { 18652034 },
  doi = { 10.1007/s00450-010-0130-4 },
  author = { Wellein and Born and Pfeifle and Duffner and Bartz },
  abstract = { In various clinical or research scenarios, such as neurosurgical intervention planning, diagnostics, or clinical studies concerning neurological diseases, cortex segmentation can be of great value. As, e.g., the visualization of the cortical surface along with target and risk structures enables conservative access planning and gives context information about the patient-specific anatomy. We present an interactive cortex segmentation pipeline (CSP) for T1-weighted MR images, utilizing watershed and level set methods. It is designed to allow the user to adjust the intermediate results at any stage of the segmentation process. Particular attention is paid to the appropriate visualization of the segmentation in the context of the original data for verification and to different interaction methods (manual editing, parameter tuning, morphological operations). Evaluation of the interactive CSP is performed with the Segmentation Validation Engine (SVE) by Shattuck et al. (NeuroImage 45(2):431-439, 2009). The segmentation quality of our method is comparable to the best results of three different established methods: the brain extraction tool (BET), brain surface extractor (BSE), and hybrid watershed algorithm (HWA). Being designed for interaction, the CSP integrates the users' expertise by allowing him to perform correction at any stage of the pipeline, enabling him to easily achieve a segmentation fulfilling his specific needs. {\textcopyright} 2010 Springer-Verlag. },
}

@article{Ramirez2011,
  year = { 2011 },
  volume = { 54 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Lesion Explorer: A comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue },
  pages = { 963--973 },
  number = { 2 },
  keywords = { Aging,Alzheimer's disease,Brain volume,Lesion analysis,Leukoariosis,MRI,Segmentation,White matter hyperintensities },
  journal = { NeuroImage },
  issn = { 10538119 },
  doi = { 10.1016/j.neuroimage.2010.09.013 },
  author = { Ramirez and Gibson and Quddus and Lobaugh and Feinstein and Levine and Scott and Levy-Cooperman and Gao and Black },
  abstract = { Subcortical hyperintensities (SH) are a commonly observed phenomenon on MRI of the aging brain (Kertesz et al., 1988). Conflicting behavioral, cognitive and pathological associations reported in the literature underline the need to develop an intracranial volumetric analysis technique to elucidate pathophysiological origins of SH in Alzheimer's disease (AD), vascular cognitive impairment (VCI) and normal aging (De Leeuw et al., 2001; Mayer and Kier, 1991; Pantoni and Garcia, 1997; Sachdev et al., 2008). The challenge is to develop processing tools that effectively and reliably quantify subcortical small vessel disease in the context of brain tissue compartments. Segmentation and brain region parcellation should account for SH subtypes which are often classified as: periventricular (pvSH) and deep white (dwSH), incidental white matter disease or lacunar infarcts and Virchow-Robin spaces. Lesion Explorer (LE) was developed as the final component of a comprehensive volumetric segmentation and parcellation image processing stream built upon previously published methods (Dade et al., 2004; Kovacevic et al., 2002). Inter-rater and inter-method reliability was accomplished both globally and regionally. Volumetric analysis showed high inter-rater reliability both globally (ICC = 99) and regionally (ICC = 98). Pixel-wise spatial congruence was also high (SI = 97). Whole brain pvSH volumes yielded high inter-rater reliability (ICC = 99). Volumetric analysis against an alternative kNN segmentation revealed high inter-method reliability (ICC = 97). Comparison with visual rating scales showed high significant correlations (ARWMC: r = 86; CHIPS: r = 87). The pipeline yields a comprehensive and reliable individualized volumetric profile for subcortical vasculopathy that includes regionalized (26 brain regions) measures for: GM, WM, sCSF, vCSF, lacunar and non-lacunar pvSH and dwSH. {\textcopyright} 2010 Elsevier Inc. },
}

@article{Oliveira2011,
  year = { 2011 },
  volume = { 10 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-79954573225{\&}doi=10.1186{\%}2F1475-925X-10-30{\&}partnerID=40{\&}md5=c81b11df842390263ed95985f684c387 },
  type = { Journal Article },
  title = { Segmentation of liver, its vessels and lesions from CT images for surgical planning },
  journal = { BioMedical Engineering Online },
  issn = { 1475925X },
  doi = { 10.1186/1475-925X-10-30 },
  author = { Oliveira and Feitosa and Correia },
  abstract = { Background: Cancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning.Methods: The proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud.Results: Experiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6thbest semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.Conclusions: The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist. The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped. {\textcopyright} 2011 Oliveira et al; licensee BioMed Central Ltd. },
}

@article{McCormick2011,
  year = { 2011 },
  volume = { 58 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956345107{\&}doi=10.1109{\%}2FTBME.2011.2106500{\&}partnerID=40{\&}md5=6d53735bfa77d8fa5f9b895224795479 },
  type = { Journal Article },
  title = { Bayesian regularization applied to ultrasound strain imaging },
  pages = { 1612--1620 },
  number = { 6 },
  keywords = { Bayes procedures,biomedical acoustic imaging,biomedical imaging,displacement measurement,image motion analysis,strain measurement },
  journal = { IEEE Transactions on Biomedical Engineering },
  issn = { 00189294 },
  doi = { 10.1109/TBME.2011.2106500 },
  author = { McCormick and Rubert and Varghese },
  abstract = { Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5 and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7 strain. It has to be noted that for applied deformations lower than 1, since signal decorrelation errors are minimal, using this approach may degrade the displacement image. {\textcopyright} 2010 IEEE. },
}

@article{Langer2011,
  year = { 2011 },
  volume = { 24 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Virtual machine performance benchmarking },
  pages = { 883--889 },
  number = { 5 },
  keywords = { Computer hardware,Computer systems,Computers in medicine },
  journal = { Journal of Digital Imaging },
  issn = { 08971889 },
  doi = { 10.1007/s10278-010-9358-6 },
  author = { Langer and French },
  abstract = { The attractions of virtual computing are many: reduced costs, reduced resources and simplified maintenance. Any one of these would be compelling for a medical imaging professional attempting to support a complex practice on limited resources in an era of ever tightened reimbursement. In particular, the ability to run multiple operating systems optimized for different tasks (computational image processing on Linux versus office tasks on Microsoft operating systems) on a single physical machine is compelling. However, there are also potential drawbacks. High performance requirements need to be carefully considered if they are to be executed in an environment where the running software has to execute through multiple layers of device drivers before reaching the real disk or network interface. Our lab has attempted to gain insight into the impact of virtualization on performance by benchmarking the following metrics on both physical and virtual platforms: local memory and disk bandwidth, network bandwidth, and integer and floating point performance. The virtual performance metrics are compared to baseline performance on "bare metal." The results are complex, and indeed somewhat surprising. {\textcopyright} Society for Imaging Informatics in Medicine 2010. },
}

@article{Garpebring2011,
  year = { 2011 },
  volume = { 24 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: Potential usefulness and limitations },
  pages = { 233--245 },
  number = { 4 },
  keywords = { Arterial input function,Dynamic contrast-enhanced MRI,Phase quantification },
  journal = { Magnetic Resonance Materials in Physics, Biology and Medicine },
  issn = { 09685243 },
  doi = { 10.1007/s10334-011-0257-8 },
  author = { Garpebring and Wirestam and Yu and Asklund and Karlsson },
  abstract = { Object: Phase-based arterial input functions (AIFs) provide a promising alternative to standard magnitude-based AIFs, for example, because inflow effects are avoided. The usefulness of phase-based AIFs in clinical dynamic contrast-enhanced MRI (DCE-MRI) was investigated, and relevant pitfalls and sources of uncertainty were identified. Materials and methods: AIFs were registered from eight human subjects on, in total, 21 occasions. AIF quality was evaluated by comparing AIFs from right and left internal carotid arteries and by assessing the reliability of blood plasma volume estimates. Results: Phase-based AIFs yielded an average bolus peak of 3.9 mM and a residual concentration of 0.37 mM after 3 min, (0.033 mmol/kg contrast agent injection). The average blood plasma volume was 2.7{\%} when using the AIF peak in the estimation, but was significantly different (p {\textless} 0.0001) and less physiologically reasonable when based on the AIF tail concentration. Motion-induced phase shifts and accumulation of contrast agent in background tissue regions were identified as main sources of uncertainty. Conclusion: Phase-based AIFs are a feasible alternative to magnitude AIFs, but sources of errors exist, making quantification difficult, especially of the AIF tail. Improvement of the technique is feasible and also required for the phase-based AIF approach to reach its full potential. {\textcopyright} 2011 ESMRMB. },
}

@article{Conzelmann2011,
  year = { 2011 },
  volume = { 108 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Neuropeptides regulate swimming depth of Platynereis larvae },
  pages = { E1174--E1183 },
  number = { 46 },
  keywords = { FMRFamide-related peptides,Neural circuit,Sensory-motor neuron,Zooplankton },
  journal = { Proceedings of the National Academy of Sciences of the United States of America },
  issn = { 00278424 },
  doi = { 10.1073/pnas.1109085108 },
  author = { Conzelmann and Offenburger and Asadulina and Keller and M{\"{u}}nch and J{\'{e}}kely },
  abstract = { Cilia-based locomotion is the major form of locomotion for microscopic planktonic organisms in the ocean. Given their negative buoyancy, these organisms must control ciliary activity to maintain an appropriate depth. The neuronal bases of depth regulation in ciliary swimmers are unknown. To gain insights into depth regulation we studied ciliary locomotor control in the planktonic larva of the marine annelid, Platynereis. We found several neuropeptides expressed in distinct sensory neurons that innervate locomotor cilia. Neuropeptides altered ciliary beat frequency and the rate of calcium-evoked ciliary arrests. These changes influenced larval orientation, vertical swimming, and sinking, resulting in upward or downward shifts in the steady-state vertical distribution of larvae. Our findings indicate that Platynereis larvae have depth-regulating peptidergic neurons that directly translate sensory inputs into locomotor output on effector cilia. We propose that the simple circuitry found in these ciliated larvae represents an ancestral state in nervous system evolution. },
}

@article{Chi2011,
  year = { 2011 },
  volume = { 58 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Segmentation of liver vasculature from contrast enhanced CT images using context-based voting },
  pages = { 2144--2153 },
  number = { 8 },
  keywords = { Liver vasculature segmentation,multiple feature point voting,vessel context,vessel junction measure },
  journal = { IEEE Transactions on Biomedical Engineering },
  issn = { 00189294 },
  doi = { 10.1109/TBME.2010.2093523 },
  author = { Chi and Liu and Venkatesh and Huang and Zhou and Tian and Nowinski },
  abstract = { A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76×0.76 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98. {\textcopyright} 2011 IEEE. },
}

@article{beare2011citk,
  year = { 2011 },
  title = { CITK-an architecture and examples of CUDA enabled ITK filters, Release 0.00 },
  journal = { The Insight Journal },
  author = { Beare and Micevski and Share and Parkinson and Ward and Goscinski and Kuiper },
}

@inproceedings{Akbarzadeh,
  year = { 2011 },
  url = { {\%}3CGo to https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858681408{\&}doi=10.1109{\%}2FNSSMIC.2011.6152682{\&}partnerID=40{\&}md5=cb5e411de14cb1afbdf27ca41e4b2746 },
  type = { Book Section },
  title = { Impact of using different tissue classes on the accuracy of MR-based attenuation correction in PET-MRI },
  series = { IEEE Nuclear Science Symposium and Medical Imaging Conference },
  publisher = { Ieee },
  pages = { 2524--2530 },
  issn = { 10957863 },
  isbn = { 9781467301183 },
  doi = { 10.1109/NSSMIC.2011.6152682 },
  booktitle = { IEEE Nuclear Science Symposium Conference Record },
  author = { Akbarzadeh and Ay and Ahmadian and {Riahi Alam} and Zaidi },
  address = { New York },
  abstract = { Diagnosis , staging and treatment of disease depends on the morphological and functional information obtained from multimodality molecular imaging systems. The combination of functional and morphological information is now routinely performed to overcome the limitations of each individual modality. Attenuation of photons in the object under study is one of the main limitations of quantitative PET imaging. Attenuation correction plays a pivotal role in PET imaging. However, the availability of CT data on hybrid PET/CT scanners made it possible to build an accurate attenuation map. One of the well-known methods for generation of the attenuation map on PE/MRI systems is MR-based attenuation correction (MRAC) where image segmentation is used to classify MRI into several classes corresponding to different attenuation factors. In this study we investigate the effect of using different numbers of classes for the generation of attenuation maps on the accuracy of attenuation correction of PET data. The study was carried out using simulations of the XCAT phantom and 10 clinical studies. For the later, CT and PET images of 10 patients were used with CT-based attenuation correction assumed as reference. MRI was classified into different classes to produce two, three and four-class attenuation maps using the ITK library. The relative error showed that the lower number of classes will increase the global error over 8{\%}. The elimination of bony structures from the attenuation map will cause a local error over 3{\%}. In clinical studies, SUV mean and SUVmax were calculated for each AC method. The results seem to indicate an underestimation of 11{\%} because of neglecting bone. {\textcopyright} 2011 IEEE. },
}

@inproceedings{Haaka,
  year = { 2011 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869068068{\&}doi=10.1109{\%}2FULTSYM.2011.0038{\&}partnerID=40{\&}md5=e5a5718535ae51415891100024bf51a5 {\%}3CGo to },
  type = { Conference Proceedings },
  title = { Spatiotemporal interpolation by normalized convolution for 4D transesophageal echocardiography },
  series = { IEEE International Ultrasonics Symposium },
  publisher = { Ieee },
  pages = { 152--155 },
  issn = { 19485719 },
  isbn = { 9781457712531 },
  doi = { 10.1109/ULTSYM.2011.0038 },
  booktitle = { IEEE International Ultrasonics Symposium, IUS },
  author = { Haak and {Van Stralen} and {Van Burken} and Klein and Pluim and {De Jong} and {Van Der Steen} and Bosch },
  address = { New York },
  abstract = { For interventional monitoring, we aim at 4D ultrasound reconstructions of structures in the beating heart from 2D transesophageal echo images by fast scan plane rotation, unsynchronized to the heart rate. For such sparsely and irregularly sampled 2D images, a special spatiotemporal interpolation approach is desired. We have previously shown the potential of spatiotemporal interpolation by normalized convolution (NC). In this work we optimized NC for our application and compared it to nearest neighbor interpolation (NN) and to temporal binning followed by linear spatial interpolation (LTB). The test datasets consisted of 600, 1350, and 1800 2D images and were derived by slicing a 4D echocardiography data sets at random rotation angle ($\theta$, range: 0-180°) and random normalized cardiac phase ($\tau$, range: 0-1). A Gaussian kernel was used for NC and optimal kernel sizes ($\sigma$ $\tau$ and $\sigma$ $\theta$) were found by performing an exhaustive search. The RMS gray value error (RMSE) of the reconstructed images was computed for all interpolation methods. The estimated optimal kernels were $\sigma$ $\theta$=3.24°/ $\sigma$ $\tau$=0.048, $\sigma$ $\theta$=2.34°/$\sigma$ $\tau$=0.026, and $\sigma$ $\theta$=1.89°/$\sigma$ $\tau$=0.023 for 600, 1350, and 1800 input images, respectively. The minimum RMSE for NC was 13.8, 10.4, and 9.4 for 600, 1350, and 1800 input images, respectively. The NN/LTB reconstruction had an RMSE of 17.8/16.4, 13.9/15.1, and 12.0/14.7 for 600, 1350, and 1800 2D input images, respectively. We showed that NC outperforms NN and LTB. For a small number of input images the advantage of NC is more pronounced. {\textcopyright} 2011 IEEE. },
}

@inproceedings{Maule,
  year = { 2011 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862235383{\&}partnerID=40{\&}md5=1d80a348add3d1bf8abea3228c5457a1 },
  type = { Conference Proceedings },
  title = { Automated approach for whole brain infarction core delineation: Using non-contrast and computed tomography angiography },
  pages = { 433--437 },
  keywords = { Acute stroke,Automated infarction core segmentation,Brain ischemia,Perfusion blood volume,Volumetric maps },
  isbn = { 9789898425799 },
  doi = { 10.5220/0003651704330437 },
  booktitle = { KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval },
  author = { Maule and Kle{\v{c}}kov{\'{a}} and Rohan },
  abstract = { This article proposes automated approach for whole brain infarction core delineation while using only non-contrast computed tomography and computed tomography angiography. The main aim is to provide additional information measuring infarction core volume while exceeding certain level is contraindication of early recanalization. Process of generation of Perfusion Blood Volume maps is described first followed by description of process of infarction core delineation. Verification of correctness is based on comparison against follow-up examinations. Discussion and future works summarizes weaknesses of the method and steps for improvement. },
}

@inproceedings{Kremer,
  year = { 2011 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869030007{\&}doi=10.1109{\%}2FULTSYM.2011.0230{\&}partnerID=40{\&}md5=785bfbda4e1f62661f6a74a21c318fe6 },
  type = { Conference Proceedings },
  title = { 2D myocardial strain in the mouse through spatial compounding: In-vivo feasibility study },
  pages = { 939--942 },
  issn = { 19485719 },
  isbn = { 9781457712531 },
  doi = { 10.1109/ULTSYM.2011.0230 },
  booktitle = { IEEE International Ultrasonics Symposium, IUS },
  author = { Kremer and Dresselaers and Heyde and Ferferieva and Caluw{\'{e}} and Choi and Oosterlinck and Janssens and Himmelreich and D'hooge },
  abstract = { Ultrasound assessment of myocardial strain can give valuable information on regional cardiac function. Speckle tracking is often used for this purpose as it can estimate the 2D myocardial strain tensor. However, in the mouse setting, speckle tracking remains challenging due to the high heart rate and the relatively thin wall compared to the typical size of the speckles. We have previously shown using simulated data sets that spatial compounding of axial velocities obtained at 3 steering angles can outperform 2D speckle tracking for 2D strain estimation in the mouse heart. In this study, beam steering was applied at -20°, 0° and 20° on short axis views of 5 control and 6 infarct mice. The lateral motion component was reconstructed through spatial compounding and results were compared to tagged $\mu$MRI. Circumferential estimates quantified by means of ultrasound and MRI could both detect regional dysfunction. Between echo and MRI, a good regression coefficient was obtained for circumferential strain estimates (r = 0.69), while radial strain estimates correlated only moderately (r = 0.37). {\textcopyright} 2011 IEEE. },
}

@techreport{Kim2011,
  year = { 2011 },
  title = { Affine Transformation for Landmark Based Registration Initializer in ITK },
  pages = { 1--8 },
  mendeley-tags = { Insight Journal },
  keywords = { Affine Transformation for Landmark Based Registrat,Insight Journal },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Engineering/Kim et al. - 2011 - Affine Transformation for Landmark Based Registration Initializer in ITK.pdf:pdf },
  booktitle = { Engineering },
  author = { Kim and Johnson and Williams and Kim and {Kim EYR Johnson HJ} },
  annote = { From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina EY Y; Johnson, Hans J.; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )


From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina EY; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )


From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )


From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )


From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K ) },
  abstract = { This document describes an affine transformation algorithm as an additional feature for landmark based registration in ITK www.itk.org. The algorithm is based on the paper by Sp{\"{a}}th, H [2]. The author derives a set of linear equations from paired landmarks and generates an affine transform from them. The method implemented here gives more freedom in the choice of registration and/or initialization method in ITK. The submission describes ITK implementation of the algorithm. },
}

@inbook{Fallavollita2010b,
  year = { 2010 },
  volume = { 7625 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Localization of brachytherapy seeds in ultrasound by registration to fluoroscopy },
  series = { Proceedings of SPIE },
  publisher = { Spie-Int Soc Optical Engineering },
  pages = { 762519 },
  issn = { 16057422 },
  isbn = { 9780819480262 },
  editor = { [object Object],[object Object] },
  doi = { 10.1117/12.844015 },
  booktitle = { Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling },
  author = { Fallavollita and KarimAghaloo and Burdette and Song and Abolmaesumi and Fichtinger },
  address = { Bellingham },
}

@inbook{Macia2010,
  year = { 2010 },
  volume = { 6279 LNAI },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { Towards a proposal for a vessel knowledge representation model },
  series = { Lecture Notes in Artificial Intelligence },
  publisher = { Springer-Verlag Berlin },
  pages = { 80--87 },
  number = { PART 4 },
  issn = { 03029743 },
  isbn = { 3642153836 },
  editor = { [object Object],[object Object],[object Object],[object Object] },
  doi = { 10.1007/978-3-642-15384-6_9 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Mac{\'{i}}a and Gra{\~{n}}a and Paloc },
  address = { Berlin },
  abstract = { We propose the development of a knowledge representation model in the area of Blood Vessel analysis, whose need we feel for the future development of the field and for our own research efforts. It will allow easy reuse of software pieces through appropriate abstractions, facilitating the development of innovative methods, procedures and applications. In this paper we present some key ideas that will be fully developed elsewhere. {\textcopyright} Springer-Verlag 2010. },
}

@article{hawley2010introduction,
  year = { 2010 },
  url = { http://ir.uiowa.edu/etd/851/ },
  title = { Introduction to ITK resample in-place image filter },
  pages = { 3--6 },
  journal = { Insight Journal },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Lu et al/Insight Journal/Lu et al. - 2010 - Introduction to ITK resample in-place image filter.pdf:pdf },
  author = { Lu and Johnson and Hawley and Johnson and Dowling and Malaterre and Greer and Salvado },
}

@article{Falck2010,
  year = { 2010 },
  volume = { 17 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952884852{\&}doi=10.1016{\%}2Fj.acra.2010.02.011{\&}partnerID=40{\&}md5=af8867fcfe3518e57c7b6076890f3032 },
  type = { Journal Article },
  title = { Semiautomated Segmentation of Pleural Effusions in MDCT Datasets },
  pages = { 841--848 },
  number = { 7 },
  keywords = { MDCT,Pleural effusion,diaphragm,segmentation,volumetry },
  journal = { Academic Radiology },
  issn = { 10766332 },
  doi = { 10.1016/j.acra.2010.02.011 },
  author = { Falck and Meier and J{\"{o}}rdens and King and Galanski and Shin },
  abstract = { Rationale and Objectives: To develop and evaluate a novel algorithm for semiautomated segmentation and volumetry of pleural effusions in multidetector computed tomography (MDCT) datasets. Materials and Methods: A seven-step algorithm for semiautomated segmentation of pleural effusions in MDCT datasets was developed, mainly using algorithms from the ITK image processing library. Semiautomated segmentation of pleural effusions was performed in 40 MDCT datasets of the chest (males = 22, females = 18, mean age: 56.7 ± 19.3 years). The accuracy of the semiautomated segmentation as compared with a manual segmentation approach was quantified based on the differences of the segmented volumes, the degree of over-/undersegmentation, and the Hausdorff distance. The time needed for the semiautomated and the manual segmentation process were recorded and compared. Results: The mean volume of the pleural effusions was 557.30 mL (± 477.27 mL) for the semiautomated and 553.19 (± 473.49 mL) for the manual segmentation. The difference was not statistically significant (Student t-test, P = .133). Regression analysis confirmed a strong relationship between the semiautomated algorithm and the gold standard (r2 = 0.998). Mean overlap of the segmented areas was 79{\%} (± 9.3{\%}) over all datasets with moderate oversegmentation (22{\%} ± 9.3{\%}) and undersegmentation (21{\%} ± 9.7{\%}). The mean Hausdorff distance was 17.2 mm (± 8.35 mm). The mean duration of the semiautomated segmentation process with user interaction was 8.4 minutes (± 2.6 minutes) as compared to 32.9 minutes (± 17.4 minutes) for manual segmentation. Conclusion: The semiautomated algorithm for segmentation and volumetry of pleural effusions in MDCT datasets shows a high diagnostic accuracy when compared with manual segmentation. {\textcopyright} 2010 AUR. },
}

@article{Riedel2010,
  year = { 2010 },
  volume = { 41 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Assessment of thrombus in acute middle cerebral artery occlusion using thin-slice nonenhanced computed tomography reconstructions },
  pages = { 1659--1664 },
  number = { 8 },
  keywords = { CT,acute care,acute stroke,embolic stroke,embolism,imaging,neuroradiology,stroke care,stroke management,thrombolysis },
  journal = { Stroke },
  issn = { 00392499 },
  doi = { 10.1161/STROKEAHA.110.580662 },
  author = { Riedel and Jensen and Rohr and Tietke and Alfke and Ulmer and Jansen },
  abstract = { Background and Purpose-: We sought to evaluate how accurately length and volume of thrombotic clots occluding cerebral arteries of patients with acute ischemic stroke can be assessed from nonenhanced CT (NECT) scans reconstructed with different slice widths. Methods-: NECT image data of 58 patients with acute ischemic stroke with vascular occlusion proven by CT angiography were reconstructed with slice widths of 1.25 mm, 2.5 mm, 3.75 mm, and 5 mm. Thrombus lengths and volumes were quantified based on these NECT images by detecting and segmenting intra-arterial hyperdensities. The results were compared with reference values of thrombus length and volume obtained from CT angiography images using Bland-Altman analysis and predefined levels or tolerance to find NECT slice thicknesses that allow for sufficiently accurate thrombus quantification. Results-: Thrombus length can be measured with high accuracy using the hyperdense middle cerebral artery sign detected in NECT images with slice thicknesses of 1.25 mm and 2.5 mm. We found mean deviations from the reference values and limits of agreement of-0.1 mm±0.6 mm with slice widths of 1.25 mm and 0.1 mm±0.7 mm for slice widths of 2.5 mm. Thrombus length measurements in NECT images with higher slice width and all evaluated thrombus volume measurements exhibited severe dependence on the level and did not match the accuracy criteria. Conclusion-: The length of the hyperdense middle cerebral artery sign as detected on thin-slice NECT reconstructions in patients with acute ischemic stroke can be used to quantify thrombotic burden accurately. Thus, it might qualify as a new diagnostic parameter in acute stroke management that indicates and quantifies the extent of vascular obliteration. {\textcopyright} 2010 American Heart Association, Inc. },
}

@article{Lucas2010,
  year = { 2010 },
  volume = { 8 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software },
  pages = { 5--17 },
  number = { 1 },
  keywords = { Image processing,MRI,Parallel processing,Pipeline,Rapid prototyping },
  journal = { Neuroinformatics },
  issn = { 15392791 },
  doi = { 10.1007/s12021-009-9061-2 },
  author = { Lucas and Bogovic and Carass and Bazin and Prince and Pham and Landman },
  abstract = { Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms. Developing custom tools and ensuring interoperability with existing tools is a significant hurdle. To address these limitations, we present a new framework for algorithm development, that implicitly ensures tool interoperability, generates graphical user interfaces, provides advanced batch processing tools, and, most importantly, requires minimal additional programming or computational overhead. Javabased rapid prototyping with this system is an efficient and practical approach to evaluate new algorithms since the proposed system ensures that rapidly constructed prototypes are actually fully-functional processing modules with support for multiple GUI's, a broad range of file formats, and distributed computation. Herein, we demonstrate MRI image processing with the proposed system for cortical surface extraction in large cross-sectional cohorts, provide a system for fully automated diffusion tensor image analysis, and illustrate how the system can be used as a simulation framework for the development of a new image analysis method. Hie system is released as open source under the Lesser GNU Public License (LGPL) through the Neuroimaging Infoimatics Tools and Resources Clearinghouse (NITRC). {\textcopyright} Springer Science+Business Media, LLC 2010. },
}

@article{Fallavollita2010,
  year = { 2010 },
  volume = { 2010 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Acquiring multiview C-arm images to assist cardiac ablation procedures },
  pages = { 10 },
  journal = { Eurasip Journal on Image and Video Processing },
  issn = { 16875176 },
  doi = { 10.1155/2010/871409 },
  author = { Fallavollita },
  abstract = { CARTO XP is an electroanatomical cardiac mapping system that provides 3D color-coded maps of the electrical activity of the heart; however it is expensive and it can only use a single costly magnetic catheter for each patient intervention. Our approach consists of integrating fluoroscopic and electrical data from the RF catheters into the same image so as to better guide RF ablation, shorten the duration of this procedure, increase its efficacy, and decrease hospital cost when compared to CARTO XP. We propose a method that relies on multi-view C-arm fluoroscopy image acquisition for (1) the 3D reconstruction of the anatomical structure of interest, (2) the robust temporal tracking of the tip-electrode of a mapping catheter between the diastolic and systolic phases and (3) the 2D/3D registration of color coded isochronal maps directly on the 2D fluoroscopy image that would help the clinician guide the ablation procedure much more effectively. The method has been tested on canine experimental data. {\textcopyright} 2010 Pascal Fallavollita. },
}

@article{Anderson2010,
  year = { 2010 },
  volume = { 12 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Using python for signal processing and visualization },
  pages = { 90--95 },
  number = { 4 },
  keywords = { Brain,Data visualization,Electroencephalography,Libraries,Magnetic resonance imaging,Python,Sensors,Signal processing,Time frequency analysis,Visualization },
  journal = { Computing in Science and Engineering },
  issn = { 15219615 },
  doi = { 10.1109/MCSE.2010.91 },
  author = { Anderson and Preston and Silva },
  abstract = { Applying Python to a neuroscience project let developers put complex data processing and advanced visualization techniques together in a coherent framework. {\textcopyright} 2006 IEEE. },
}

@inproceedings{Bernardini,
  year = { 2010 },
  volume = { 7723 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954408709{\&}doi=10.1117{\%}2F12.854027{\&}partnerID=40{\&}md5=62af8ffb1680932adc4db6fa28a2b4f1 },
  type = { Conference Proceedings },
  title = { An automated real-time microscopy system for analysis of fluorescence resonance energy transfer },
  pages = { 772311 },
  issn = { 0277786X },
  isbn = { 9780819481962 },
  doi = { 10.1117/12.854027 },
  booktitle = { Optics, Photonics, and Digital Technologies for Multimedia Applications },
  author = { Bernardini and Wotzlaw and Lipinski and Fandrey },
  abstract = { Molecular imaging based on Fluorescence Resonance Energy Transfer (FRET) is widely used in cellular physiology both for protein-protein interaction analysis and detecting conformational changes of single proteins, e.g. during activation of signaling cascades. However, getting reliable results from FRET measurements is still hampered by methodological problems such as spectral bleed through, chromatic aberration, focal plane shifts and false positive FRET. Particularly false positive FRET signals caused by random interaction of the fluorescent dyes can easily lead to misinterpretation of the data. This work introduces a Nipkow Disc based FRET microscopy system, that is easy to operate without expert knowledge of FRET. The system automatically accounts for all relevant sources of errors and provides various result presentations of two, three and four dimensional FRET data. Two examples are given to demonstrate the scope of application. An interaction analysis of the two subunits of the hypoxia-inducible transcription factor 1 demonstrates the use of the system as a tool for protein-protein interaction analysis. As an example for time lapse observations, the conformational change of the fluorophore labeled heat shock protein 33 in the presence of oxidant stress is shown. },
}

@book{Mandl2009,
  year = { 2009 },
  url = { {\%}3CGo to },
  type = { Book },
  title = { TOWARDS A NUMERICAL 3D MODEL OF FUNCTIONAL ELECTRICAL STIMULATION OF DENERVATED, DEGENERATED HUMAN SKELETAL MUSCLE },
  series = { Emss 2009: 21st European Modeling and Simulation Symposium, Vol Ii },
  publisher = { Univ De La Laguna },
  pages = { 209--+ },
  isbn = { 978-84-692-5415-8 },
  author = { Mandl and Martinek and Mayr and Rattay and Reichel and Moser },
  address = { La Laguna },
}

@inbook{Corradi2009,
  year = { 2009 },
  volume = { 147 },
  url = { {\%}3CGo to },
  type = { Book Section },
  title = { XTENS - An eXTensible Environment for NeuroScience },
  series = { Studies in Health Technology and Informatics },
  publisher = { Ios Press },
  pages = { 127--136 },
  keywords = { Collaborative environment,Data integration,Epilepsy,Grid,Multimodal and multiscale analysis,Remote visualization,Surgical planning },
  issn = { 18798365 },
  isbn = { 9781607500278 },
  editor = { [object Object],[object Object],[object Object],[object Object],[object Object],[object Object] },
  doi = { 10.3233/978-1-60750-027-8-127 },
  booktitle = { Studies in Health Technology and Informatics },
  author = { Corradi and Arnulfo and Schenone and Porro and Fato },
  address = { Amsterdam },
  abstract = { The XTENS (eXTensible Environment for NeuroScience) platform consists in an highly extensible environment for collaborative work that improve repeatability of experiment and provides data storage and analysis capabilities. The platform is divided in repository and application domains, branched in services with different purpose. The first domain is the central component of the platform and consists in a multimodal repository with a client-server architecture. The second one provides remote tools for image and signal visualization and analysis. The main issue for such a platform is not only to provide an extensible collaborative environment, but also to build a development platform for testing models and algorithms in neuroscience. For these reasons a Grid approach has been considered. Both computational and data Grids infrastructures can be exploited to analyze and share large datasets of distributed data. The architecture has been deployed to support surgical planning for patients affected by drug resistant epilepsy. In that scenario, a complex analysis for a fully multimodal dataset including different image modalities, EEG and video is required to localize the origin of the ictal discharge and critical brain areas. As first results, prototype versions of both repository and application domain components are presented. {\textcopyright} 2009 The authors and IOS Press. All rights reserved. },
}

@article{Song2009,
  year = { 2009 },
  volume = { 25 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650586605{\&}partnerID=40{\&}md5=047142ba89c1419e7f5474c726cfdc20 },
  type = { Journal Article },
  title = { Virtual surgery based on medical images },
  pages = { 1481--1484 },
  number = { 8 },
  keywords = { Image process,Virtual segmentation,Virtual surgery,Visualization },
  journal = { Chinese Journal of Medical Imaging Technology },
  issn = { 10033289 },
  author = { Song and Luo and Wen },
  abstract = { Objective: To establish a virtual surgery system based on three-dimensional visualization technique which can help the doctors to plot the surgery. Methods: A curve was created on the 3D space, and some points added on the curve adjusted by controlling the points which could be added and deleted. The angle could be computed according to the final curves, thus to place the scalpel to give convenience to the real surgery. Results: The virtual surgery system was used in hospital, and the results were satisfied. Conclusion: The virtual surgery system added to medical image process system is helpful to decrease risk of surgical operation. },
}

@article{Roach2009,
  year = { 2009 },
  volume = { 25 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { High throughput single cell bioinformatics },
  pages = { 1772--1779 },
  number = { 6 },
  keywords = { Cytometry,Free radicals,Hepatocytes,Membrane potential,Microfabrication,Microwells,Mitochondria },
  journal = { Biotechnology Progress },
  issn = { 87567938 },
  doi = { 10.1002/btpr.289 },
  author = { Roach and King and Uygun and Kohane and Yarmush and Toner },
  abstract = { Advances in systems biology and bioinformatics have highlighted that no cell population is truly uniform and that stochastic behavior is an inherent property of many biological systems. As a result, bulk measurements can be misleading even when particular care has been taken to isolate a single cell type, and measurements averaged over multiple cell populations in a tissue can be as misleading as the average height at an elementary school. There is a growing need for experimental techniques that can provide a combination of single cell resolution, large cell populations, and the ability to track cells over multiple time points. In this article, a microwell array cytometry platform was developed to meet this need and investigate the heterogeneity and stochasticity of cell behavior on a single cell basis. The platform consisted of a microfabricated device with high-density arrays of cell-sized microwells and custom software for automated image processing and data analysis. As a model experimental system, we used primary hepatocytes labeled with fluorescent probes sensitive to mitochondrial membrane potential and free radical generation. The cells were exposed to oxidative stress and the responses were dynamically monitored for each cell. The resulting data was then analyzed using bioinformatics techniques such as hierarchical and k-means clustering to visualize the data and identify interesting features. The results showed that clustering of the dynamic data not only enhanced comparisons between the treatment groups but also revealed a number of distinct response patterns within each treatment group. Heatmaps with hierarchical clustering also provided a data-rich complement to survival curves in a dose response experiment. The microwell array cytometry platform was shown to be powerful, easy to use, and able to provide a detailed picture of the heterogeneity present in cell responses to oxidative stress. We believe that our microwell array cytometry platform will have general utility for a wide range of questions related to cell population heterogeneity, biological stochasticity, and cell behavior under stress conditions. {\textcopyright} 2009 American Institute of Chemical Engineers. },
}

@article{Mosaliganti2009,
  year = { 2009 },
  volume = { 13 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Tensor classification of N-point correlation function features for histology tissue segmentation },
  pages = { 156--166 },
  number = { 1 },
  keywords = { Image segmentation,Microstructure,N-point correlation functions,Phenotyping },
  journal = { Medical Image Analysis },
  issn = { 13618415 },
  doi = { 10.1016/j.media.2008.06.020 },
  author = { Mosaliganti and Janoos and Irfanoglu and Ridgway and Machiraju and Huang and Saltz and Leone and Ostrowski },
  abstract = { In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure. Using a variant of higher-order singular value decomposition (HOSVD) algorithm, we realize a robust classifier that provides a multi-linear description of the tensor feature space. Validated results of the segmentation are presented in a case-study that focuses on understanding the genetic phenotyping differences in mouse placentae. {\textcopyright} 2008 Elsevier B.V. All rights reserved. },
}

@article{Fang2009,
  year = { 2009 },
  volume = { 28 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149166606{\&}doi=10.1109{\%}2FTMI.2008.925082{\&}partnerID=40{\&}md5=0adeb1e35532e1ca200be1b4fda2df76 },
  type = { Journal Article },
  title = { Combined optical imaging and mammography of the healthy breast: Optical contrast derived from breast structure and compression },
  pmid = { 19116186 },
  pages = { 30--42 },
  number = { 1 },
  keywords = { Breast imaging,Multimodality imaging,Tomography },
  journal = { IEEE Transactions on Medical Imaging },
  issn = { 02780062 },
  doi = { 10.1109/TMI.2008.925082 },
  author = { Fang and Carp and Selb and Boverman and Zhang and Kopans and Moore and Miller and Brooks and Boas },
  abstract = { In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 $\mu$ and 71{\%}, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1 ± 6.1 $\mu$ for fibroglandular tissue, 15.4 ± 5.0 $\mu$ for adipose, and 22.2 ± 7.3 $\mu$ for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p≤ 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis. {\textcopyright} 2006 IEEE. },
}

@incollection{Scheenstra2009,
  year = { 2009 },
  volume = { 5636 LNCS },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349313285{\&}doi=10.1007{\%}2F978-3-642-02498-6{\_}47{\&}partnerID=40{\&}md5=6667d3c02eda486716d2cfb5e09e139d },
  type = { Serial },
  title = { The 3D moore-rayleigh test for the quantitative groupwise comparison of MR brain images },
  pages = { 564--575 },
  issn = { 03029743 },
  isbn = { 3642024971 },
  doi = { 10.1007/978-3-642-02498-6_47 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Scheenstra and Muskulus and Staring and {Van Den Maagdenberg} and {Verduyn Lunel} and Reiber and {Van Der Weerd} and Dijkstra },
  abstract = { Non-rigid registration of MR images to a common reference image results in deformation fields, from which anatomical differences can be statistically assessed, within and between populations. Without further assumptions, nonparametric tests are required and currently the analysis of deformation fields is performed by permutation tests. For deformation fields, often the vector magnitude is chosen as test statistic, resulting in a loss of information. In this paper, we consider the three dimensional Moore-Rayleigh test as an alternative for permutation tests. This nonparametric test offers two novel features: first, it incorporates both the directions and magnitude of the deformation vectors. Second, as its distribution function is available in closed form, this test statistic can be used in a clinical setting. Using synthetic data that represents variations as commonly encountered in clinical data, we show that the Moore-Rayleigh test outperforms the classical permutation test. {\textcopyright} 2009 Springer Berlin Heidelberg. },
}

@inproceedings{Mandl,
  year = { 2009 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874185461{\&}partnerID=40{\&}md5=a9627e50e42db43cbfa2f5ca618bdad4 {\%}3CGo to },
  type = { Book },
  title = { Towards a numerical 3D model of functional electrical stimulation of denervated, degenerated human skeletal muscle },
  series = { Emss 2009: 21st European Modeling and Simulation Symposium, Vol Ii },
  publisher = { Univ De La Laguna },
  pages = { 209--+ },
  keywords = { Finite difference method,Finite element method,Functional electrical stimulation,Patient specific model },
  isbn = { 978-84-692-5415-8 },
  booktitle = { 21st European Modeling and Simulation Symposium, EMSS 2009 },
  author = { Mandl and Martinek and Mayr and Rattay and Reichel and Moser },
  address = { La Laguna },
  abstract = { Functional electrical stimulation (FES) of longterm denervated, degenerated human skeletal muscle has proven to be an effective method for improving a number of physiological parameters. In order to derive suitable stimulation configurations (electrode position and size) for certain muscle specific training tasks, the activation pattern induced by a given configuration must be known. The probability of activation can be estimated by activating functions which depend on the distribution of the externally applied electrical field. We thus chose to create both, a finite element (FE) and a finite difference (FD) model of the field distribution to simulate and study activation patterns and to compare their efficiency and feasibility. First preliminary results show good agreement between the two modeling approaches. },
}

@inproceedings{Oliveira,
  year = { 2009 },
  volume = { 2 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349298510{\&}partnerID=40{\&}md5=921f6aa504ccbbbf03902b5d0fbb632b },
  type = { Conference Proceedings },
  title = { Liver segmentation using level sets and genetic algorithms },
  pages = { 154--159 },
  keywords = { Computer tomography,Genetic algorithms,Level sets,Liver segmentation,Medical imaging },
  isbn = { 9789898111692 },
  doi = { 10.5220/0001787401540159 },
  booktitle = { VISAPP 2009 - Proceedings of the 4th International Conference on Computer Vision Theory and Applications },
  author = { Oliveira and Feitosa and Correia },
  abstract = { This paper presents a method based on level sets to segment the liver using Computer Tomography (CT) images. Initially, the liver boundary is manually set in one slice as an initial solution, and then the method automatically segments the liver in all other slices, sequentially. In each step of iteration it fits a Gaussian curve to the liver histogram to model the speed image in which the level sets propagates. The parameters of our method were estimated using Genetic Algorithms (GA) and a database of reference segmentations. The method was tested using 20 different exams and five different measures of performance, and the results obtained confirm the potential of the method. The cases in which the method presented a poor performance are also discussed in order to instigate further research. },
}

@article{powell2008registration,
  year = { 2008 },
  volume = { 39 },
  url = { http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2253948{\&}tool=pmcentrez{\&}rendertype=abstract http://www.sciencedirect.com/science?{\_}ob=ArticleURL{\&}{\_}udi=B6WNP-4PGGP30-3{\&}{\_}user=440026{\&}{\_}rdoc=1{\&}{\_}fmt={\&}{\_}orig=search{\&}{\_}sort=d{\&}view=c{\&}{\_}acct=C000020939{\&}{\_}version= },
  title = { Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures },
  publisher = { Academic Press },
  pmid = { 17904870 },
  pages = { 238--247 },
  number = { 1 },
  month = { jan },
  keywords = { Artificial neural networks,Brain segmentation,MRI,Registration-based segmentation,Support vector machine },
  journal = { NeuroImage },
  issn = { 10538119 },
  isbn = { 1053-8119 (Print) },
  file = { :Users/johnsonhj/Documents/Mendeley Desktop/Powell et al/NeuroImage/Powell et al. - 2008 - Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures.pdf:pdf },
  eprint = { NIHMS150003 },
  doi = { 10.1016/j.neuroimage.2007.05.063 },
  author = { Powell and Magnotta and Johnson and Jammalamadaka and Pierson and Andreasen },
  arxivid = { NIHMS150003 },
  archiveprefix = { arXiv },
  annote = { From Duplicate 1 (Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures - Powell, Stephanie; Magnotta, Vincent A.; Johnson, Hans J.; Jammalamadaka, Vamsi K.; Pierson, Ronald K.; Andreasen, Nancy C.)

From Duplicate 2 (Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures - Powell, Stephanie; Magnotta, Vincent A.; Johnson, Hans J.; Jammalamadaka, Vamsi K.; Pierson, Ronald K.; Andreasen, Nancy C.)

{\#}{\#}CONTRIBUTIONS: As the directory of medical imaging for the department of Psychiatry I work closely with neuroscience student and junior faculty to mentor on appropriate medical imaging analysis techniques.  I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures - Powell, Stephanie; Magnotta, Vincent A.; Johnson, Hans J.; Jammalamadaka, Vamsi K.; Pierson, Ronald K.; Andreasen, Nancy C.)

{\#}{\#}CONTRIBUTIONS: As the directory of medical imaging for the department of Psychiatry I work closely with neuroscience student and junior faculty to mentor on appropriate medical imaging analysis techniques.  I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#} 
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#} },
  abstract = { The large amount of imaging data collected in several ongoing multi-center studies requires automated methods to delineate brain structures of interest. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures. Here we present several automated segmentation methods using multidimensional registration. A direct comparison between template, probability, artificial neural network (ANN) and support vector machine (SVM)-based automated segmentation methods is presented. Three metrics for each segmentation method are reported in the delineation of subcortical and cerebellar brain regions. Results show that the machine learning methods outperform the template and probability-based methods. Utilization of these automated segmentation methods may be as reliable as manual raters and require no rater intervention. {\textcopyright} 2007 Elsevier Inc. All rights reserved. },
}

@article{Reitz2008,
  year = { 2008 },
  volume = { 53 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-39049165539{\&}doi=10.1088{\%}2F0031-9155{\%}2F53{\%}2F4{\%}2F001{\&}partnerID=40{\&}md5=1053838180560cce8431007aee909ef5 },
  type = { Journal Article },
  title = { Monitoring tumor motion with on-line mega-voltage cone-beam computed tomography imaging in a cine mode },
  pages = { 823--836 },
  number = { 4 },
  journal = { Physics in Medicine and Biology },
  issn = { 00319155 },
  doi = { 10.1088/0031-9155/53/4/001 },
  author = { Reitz and Gayou and Parda and Miften },
  abstract = { Accurate daily patient localization is becoming increasingly important in external-beam radiotherapy (RT). Mega-voltage cone-beam computed tomography (MV-CBCT) utilizing a therapy beam and an on-board electronic portal imager can be used to localize tumor volumes and verify the patient's position prior to treatment. MV-CBCT produces a static volumetric image and therefore can only account for inter-fractional changes. In this work, the feasibility of using the MV-CBCT raw data as a fluoroscopic series of portal images to monitor tumor changes due to e.g. respiratory motion was investigated. A method was developed to read and convert the CB raw data into a cine. To improve the contrast-to-noise ratio on the MV-CB projection data, image post-processing with filtering techniques was investigated. Volumes of interest from the planning CT were projected onto the MV-cine. Because of the small exposure and the varying thickness of the patient depending on the projection angle, soft-tissue contrast was limited. Tumor visibility as a function of tumor size and projection angle was studied. The method was well suited in the upper chest, where motion of the tumor as well as of the diaphragm could be clearly seen. In the cases of patients with non-small cell lung cancer with medium or large tumor masses, we verified that the tumor mass was always located within the PTV despite respiratory motion. However for small tumors the method is less applicable, because the visibility of those targets becomes marginal. Evaluation of motion in non-superior-inferior directions might also be limited for small tumor masses. Viewing MV-CBCT data in a cine mode adds to the utility of MV-CBCT for verification of tumor motion and for deriving individualized treatment margins. {\textcopyright} 2008 Institute of Physics and Engineering in Medicine. },
}

@article{Peng2008,
  year = { 2008 },
  volume = { 24 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Bioimage informatics: A new area of engineering biology },
  pages = { 1827--1836 },
  number = { 17 },
  journal = { Bioinformatics },
  issn = { 13674803 },
  doi = { 10.1093/bioinformatics/btn346 },
  author = { Peng },
  abstract = { In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called 'bioimage informatics'. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources. {\textcopyright} 2008 The Author(s). },
}

@inproceedings{Fanga,
  year = { 2008 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884324617{\&}partnerID=40{\&}md5=4341cad05ad6059b2b7866085d7c9037 },
  type = { Conference Proceedings },
  title = { Spectrally constrained optical breast imaging with coregistered x-ray tomosynthesis },
  doi = { 10.1364/biomed.2008.bsub2 },
  booktitle = { Biomedical Optics, BIOMED 2008 },
  author = { Fang and Carp and Selb and Moore and Kopans and Miller and Brooks and Boas },
  abstract = { We imaged 65 patients with a combined optical and tomosynthesis imaging system. The bulk optical properties from 72 healthy breasts and the reconstructed images using a spectrally-constrained algorithm for healthy and tumor breasts are reported. {\textcopyright} 2007 Optical Society of America. },
}

@inproceedings{Calvini,
  year = { 2008 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-67649172535{\&}doi=10.1109{\%}2FNSSMIC.2008.4774245{\&}partnerID=40{\&}md5=0876425a595d10f8641904244780ec81 },
  type = { Conference Proceedings },
  title = { Automatic localization of the hippocampal region in MR images to asses early diagnosis of Alzheimer's disease in MCI patients },
  pages = { 4348--4354 },
  issn = { 10957863 },
  isbn = { 9781424427154 },
  doi = { 10.1109/NSSMIC.2008.4774245 },
  booktitle = { IEEE Nuclear Science Symposium Conference Record },
  author = { Calvini and Chincarini and Donadio and Gemme and Squarcia and Nobili and Rodriguez and Bellotti and Catanzariti and Cerello and {De Mitri} and Fantacci },
  abstract = { Atrophy and other brain changes, which are typical of aging, generate wide inter-individual variability of morphology in the medial temporal lobe (MTL), including the hippocampal formation. Starting from a sample population of 133 MR images we developed a procedure that extracts from each MR two sub images, containing the hippocampal formations plus a portion of the adjacent tissues and cavities. Then, a small number of templates is selected among the previously obtained sub images, able to describe the morphological variability present in the whole population. Finally an automatic procedure is prepared which, on the basis of the given set of templates, is able to find both hippocampal formations in any new MR image. MR images ranging from normalcy to extreme atrophy can be successfully processed. The proposed approach, besides being a preliminary step towards the unsupervised segmentation of the hippocampus, extracts from the MR image information useful for diagnostic purposes and, in particular, could give the possibility of performing morphometric studies on the medial temporal lobe in an automated way. The automated analysis of MTL atrophy in the segmented volume is readily applied to the early assessment of Alzheimer Disease (AD), leading to discriminating converters from Mild Cognitive Impairment (MCI) to AD with an average three years follow-up. This procedure can quickly and reliably provide additional information in early diagnosis of AD. {\textcopyright} 2008 IEEE. },
}

@article{Rasmussen2007,
  year = { 2007 },
  volume = { 149 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Functional neuronavigation combined with intra-operative 3D ultrasound: Initial experiences during surgical resections close to eloquent brain areas and future directions in automatic brain shift compensation of preoperative data },
  pages = { 365--378 },
  number = { 4 },
  keywords = { Brain shift,Diffusion tensor imaging,Functional magnetic resonance imaging,Image guidance,Image registration,Intra-operative 3D ultrasound,Minimally invasive surgery,Multimodal imaging,Neuronavigation },
  journal = { Acta Neurochirurgica },
  issn = { 00016268 },
  doi = { 10.1007/s00701-006-1110-0 },
  author = { Rasmussen and Lindseth and Rygh and Berntsen and Selbekk and Xu and {Nagelhus Hernes} and Harg and H{\aa}berg and Unsgaard },
  abstract = { Objective. The aims of this study were: 1) To develop protocols for, integration and assessment of the usefulness of high quality fMRI (functional magnetic resonance imaging) and DTI (diffusion tensor imaging) data in an ultrasound-based neuronavigation system. 2) To develop and demonstrate a co-registration method for automatic brain-shift correction of pre-operative MR data using intra-operative 3D ultrasound. Methods. Twelve patients undergoing brain surgery were scanned to obtain structural and fMRI data before the operation. In six of these patients, DTI data was also obtained. The preoperative data was imported into a commercial ultrasound-based navigation system and used for surgical planning and guidance. Intra-operative ultrasound volumes were acquired when needed during surgery and the multimodal data was used for guidance and resection control. The use of the available image information during planning and surgery was recorded. An automatic voxel-based registration method between preoperative MRA and intra-operative 3D ultrasound angiography (Power Doppler) was developed and tested postoperatively. Results. The study showed that it is possible to implement robust, high-quality protocols for fMRI and DTI and that the acquired data could be seamlessly integrated in an ultrasound-based neuronavigation system. Navigation based on fMRI data was found to be important for pre-operative planning in all twelve procedures. In five out of eleven cases the data was also found useful during the resection. DTI data was found to be useful for planning in all five cases where these data were imported into the navigation system. In two out of four cases DTI data was also considered important during the resection (in one case DTI data were acquired but not imported and in another case fMRI and DTI data could only be used for planning). Information regarding the location of important functional areas (fMRI) was more beneficial during the planning phase while DTI data was more helpful during the resection. Furthermore, the surgeon found it more user-friendly and efficient to interpret fMRI and DTI information when shown in a navigation system as compared to the traditional display on a light board or monitor. Updating MRI data for brain-shift using automatic co-registration of preoperative MRI with intra-operative ultrasound was feasible. Conclusion. In the present study we have demonstrated how both fMRI and DTI data can be acquired and integrated into a neuronavigation system for improved surgical planning and guidance. The surgeons reported that the integration of fMRI and DTI data in the navigation system represented valuable additional information presented in a user-friendly way and functional neuronavigation is now in routine use at our hospital. Furthermore, the present study showed that automatic ultrasound-based updates of important pre-operative MRI data are feasible and hence can be used to compensate for brain shift. {\textcopyright} 2007 Springer-Verlag. },
}

@article{BoisdAische2007,
  year = { 2007 },
  volume = { 2 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-34248594102{\&}doi=10.1016{\%}2Fj.bspc.2007.03.002{\&}partnerID=40{\&}md5=845c913387e22bc8438d1efee07c729f },
  type = { Journal Article },
  title = { Estimation of the deformations induced by articulated bodies: Registration of the spinal column },
  pages = { 16--24 },
  number = { 1 },
  keywords = { Articulation,Registration,Vertebrae },
  journal = { Biomedical Signal Processing and Control },
  issn = { 17468094 },
  doi = { 10.1016/j.bspc.2007.03.002 },
  author = { Bois d'Aische and {De Craene} and Geets and Gr{\'{e}}goire and Macq and Warfield },
  abstract = { We present a new non-rigid registration algorithm estimating the displacement field generated by articulated bodies. Indeed the bony structures between different patient images may rigidly move while other tissues may deform in a more complex way. Our algorithm tracks the displacement induced in the column by a movement of the patient between two acquisitions. The volumetric deformation field in the whole body is then inferred from those displacements using a linear elastic biomechanical finite element model. We demonstrate in this paper that this method provides accurate results on 3D sets of computed tomography (CT), MR and positron emission tomography (PET) images and that the results of the registration algorithm show significant decreases in the mean, min and max errors. {\textcopyright} 2007 Elsevier Ltd. All rights reserved. },
}

@inproceedings{Ali2007,
  year = { 2007 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-36348985016{\&}doi=10.1109{\%}2FISBI.2007.356787{\&}partnerID=40{\&}md5=dd565221c35ae17147c212afbaded4f7 {\%}3CGo to },
  type = { Conference Proceedings },
  title = { Phase-based segmentation of cells from brightfield microscopy },
  series = { IEEE International Symposium on Biomedical Imaging },
  publisher = { Ieee },
  pages = { 57--60 },
  keywords = { Biomedical image processing,Image segmentation,Microscopy },
  isbn = { 1424406722 },
  doi = { 10.1109/ISBI.2007.356787 },
  booktitle = { 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings },
  author = { Ali and Gooding and Christlieb and Brady },
  address = { New York },
  abstract = { Segmentation of transparent cells in brightfield microscopy images could facilitate the quantitative analysis of corresponding fluorescence images. However, this presents a challenge due to irregular morphology and weak intensity variation, particularly in ultra-thin regions. A boundary detection technique is applied to a series of variable focus images whereby a level set contour is initialised on a defocused image with improved intensity contrast, and subsequently evolved towards the correct boundary using images of improving focus. Local phase coherence is used to identify features within the images, driving contour evolution particularly in near-focus images which lack intensity contrast. Preliminary results demonstrate the effectiveness of this approach in segmenting the main cell body regions. {\textcopyright} 2007 IEEE. },
}

@article{Papademetris2006,
  year = { 2006 },
  url = { http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.7477{\&}rep=rep1{\&}type=pdf },
  title = { An Introduction to Programming for medical image Analysis with the visualization Toolkit },
  pages = { 283 },
  journal = { Yale University },
  author = { Papademetris and Joshi },
  abstract = { This book is an edited collection of class handouts that was written for the graduate seminar “Programming for Medical Image Analysis” (ENAS 920a). This class was taught at Yale University, Department of Biomedical Engineering, in the Fall of 2006 and again in the Spring 2009 semester. Some the comments in this draft version of the book reflect this fact. For example, see comments beginning “at Yale”. Furthermore, many of the references that will appear in the final version are still omitted. It is made available at this stage in the hope that it will be useful. },
}

@article{Yin2006,
  year = { 2006 },
  volume = { 16 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645088409{\&}doi=10.1016{\%}2Fj.semradonc.2005.12.004{\&}partnerID=40{\&}md5=1274c24db6658fb341f2b0b1f475f7ce },
  type = { Journal Article },
  title = { Physics and imaging for targeting of oligometastases },
  pages = { 85--101 },
  number = { 2 },
  journal = { Seminars in Radiation Oncology },
  issn = { 10534296 },
  doi = { 10.1016/j.semradonc.2005.12.004 },
  author = { Yin and Das and Kirkpatrick and Oldham and Wang and Zhou },
  abstract = { Oligometastases refer to metastases that are limited in number and location and are amenable to regional treatment. The majority of these metastases appear in the brain, lung, liver, and bone. Although the focus of interest in the past within radiation oncology has been on the treatment of intracranial metastases, there has been growing interest in extracranial sites such as the liver and lung. This is largely because of the rapid development of targeting techniques for oligometastases such as intensity-modulated and image-guided radiation therapy, which has made it possible to deliver single or a few fractions of high-dose radiation treatments, highly conformal to the target. The clinical decision to use radiation to treat oligometastases is based on both radiobiological and physics considerations. The radiobiological considerations involve improvement of treatment schema for time, dose, and volume. Areas of interests are hypofractionation, tumor and normal tissue tolerance, and hypoxia. The physics considerations for oligometastases treatment are focused mainly on ensuring treatment accuracy and precision. This article discusses the physics and imaging aspects involved in each step of the radiation treatment process for oligometastases, including target definition, treatment simulation, treatment planning, pretreatment target localization, radiation delivery, treatment verification, and treatment evaluation. {\textcopyright} 2006 Elsevier Inc. All rights reserved. },
}

@article{Qiao2006,
  year = { 2006 },
  volume = { 51 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-33746512521{\&}doi=10.1088{\%}2F0031-9155{\%}2F51{\%}2F15{\%}2F012{\&}partnerID=40{\&}md5=85744da7c87fdff0c273cb687cee3723 },
  type = { Journal Article },
  title = { A motion-incorporated reconstruction method for gated PET studies },
  pages = { 3769--3783 },
  number = { 15 },
  journal = { Physics in Medicine and Biology },
  issn = { 00319155 },
  doi = { 10.1088/0031-9155/51/15/012 },
  author = { Qiao and Pan and Clark and Mawlawi },
  abstract = { Cardiac and respiratory motion artefacts in PET imaging have been traditionally resolved by acquiring the data in gated mode. However, gated PET images are usually characterized by high noise content due to their low photon statistics. In this paper, we present a novel 4D model for the PET imaging system, which can incorporate motion information to generate a motion-free image with all acquired data. A computer simulation and a phantom study were conducted to test the performance of this approach. The computer simulation was based on a digital phantom that was continuously scaled during data acquisition. The phantom study, on the other hand, used two spheres in a tank of water, all of which were filled with 18F water. One of the spheres was stationary while the other moved in a sinusoidal fashion to simulate tumour motion in the thorax. Data were acquired using both 4D CT and gated PET. Motion information was derived from the 4D CT images and then used in the 4D PET model. Both studies showed that this 4D PET model had a good motion-compensating capability. In the phantom study, this approach reduced quantification error of the radioactivity concentration by 95{\%} when compared to a corresponding static acquisition, while signal-to-noise ratio was improved by 210{\%} when compared to a corresponding gated image. {\textcopyright} 2006 IOP Publishing Ltd. },
}

@article{Hajdu2006,
  year = { 2006 },
  volume = { 12 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { The "MEDIP - Platform independent software system for medical image processing" project },
  pages = { 1229--1239 },
  number = { 9 },
  keywords = { Medical image processing,Multimodal image analysis,Surface rendering,Virtual surgery,Visualization,Volume rendering },
  journal = { Journal of Universal Computer Science },
  issn = { 0958695X },
  doi = { 10.3217/jucs-012-09-1229 },
  author = { Hajdu and Kormos and Lencse and Tr{\'{o}}n and Emri },
  abstract = { In this paper we present the structure and the achieved results of the R{\&}D project IKTA-4, 6/2001 "MEDIP - Platform independent software system for medical image processing" supported by the Hungarian Ministry of Education. The aim of the project was to develop a software background for our basic and applied research in the field of medical imaging that can be used in clinical routine, as well. Realization was based on the experience of information technology and medical imaging research university teams and a company specialized on software and hardware developing for nuclear medicine. The aims also reflect some former research and development activities of the participants. Thus some of them are well experienced in registration, segmentation and image fusion techniques. These experiences were also considered in the determination of the main purposes. The capabilities of the provided software library were demonstrated through test applications from the fields of orthopedics, oncology and nuclear medicine. {\textcopyright} J.UCS. },
}

@article{RN784,
  year = { 2006 },
  volume = { 27 },
  url = { https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.20216 },
  type = { Journal Article },
  title = { Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images },
  pages = { 747--754 },
  number = { 9 },
  keywords = { Atlas-based segmentation,Deformable model,Image registration,fMRI },
  journal = { Human Brain Mapping },
  issn = { 10659471 },
  doi = { 10.1002/hbm.20216 },
  author = { Wu and Carmichael and Lopez-Garcia and Carter and Aizenstein },
  abstract = { Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited-dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel-level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM. {\textcopyright} 2006 Wiley-Liss, Inc. },
}

@inproceedings{Muller2006,
  year = { 2006 },
  volume = { 4182 LNCS },
  title = { Automated object extraction for medical image retrieval using the Insight Toolkit (ITK) },
  pages = { 476--488 },
  issn = { 16113349 },
  isbn = { 3540457801 },
  doi = { 10.1007/11880592_36 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { M{\"{u}}ller and Heuberger and Depeursinge and Geissb{\"{u}}hler },
  abstract = { Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has the potential to help better managing the rising amount of visual medical data. One of the most frequent application fields for content-based medical image retrieval (CBIR) is diagnostic aid. By submitting an image showing a certain pathology to a CBIR system, the medical expert can easily find similar cases. A major problem is the background surrounding the object in many medical images. System parameters of the imaging modalities are stored around the images in text as well as patient name or a logo of the institution. With such noisy input data, image retrieval often rather finds images where the object appears in the same area and is surrounded by similar structures. Whereas in specialised application domains, segmentation can focus the research on a particular area, PACS-like (Picture Archiving and Communication System) databases containing a large variety of images need a more general approach. This article describes an algorithm to extract the important object of the image to reduce the amount of data to be analysed for CBIR and focuses analysis to the important object. Most current solutions index the entire image without making a difference between object and background when using varied PACS-like databases or radiology teaching files. Our requirement is to have a fully automatic algorithm for object extraction. Medical images have the advantage to normally have one particular object more or less in the centre of the image. The database used for evaluating this task is taken from a radiology teaching file called casimage and the retrieval component is an open source retrieval engine called medGIFT. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006. },
}

@inproceedings{Stevensona,
  year = { 2006 },
  volume = { 6143 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745353456{\&}doi=10.1117{\%}2F12.654301{\&}partnerID=40{\&}md5=595f4e12f1f8758a3702a5ddb70cd256 },
  type = { Conference Proceedings },
  title = { Multi-angle deformation analysis of Hoffa's fat pad },
  pages = { 614329 },
  issn = { 16057422 },
  isbn = { 0819461865 },
  doi = { 10.1117/12.654301 },
  booktitle = { Medical Imaging 2006: Physiology, Function, and Structure from Medical Images },
  author = { Stevenson and Schweitzer and Hamarneh },
  abstract = { Recent advances in medical research hypothesize that certain body fat, in addition to having a classical role of energy storage, may also have mechanical function. In particular, we analyzed the infrapatellar fat pad of Hoffa using 3D CT images of the knee at multiple angles to determine how the fat pad changes shape as the knee bends and whether the fat pad provides cushioning in the knee joint. The images were initially processed using a median filter then segmented using a region growing technique to isolate the fat pad from the rest of the knee. Next, rigid registration was performed to align the series of images to match the reference image. Finally, multi-resolution FEM registration was completed between the aligned images. The resulting displacements fields were used to determine the local volume change of the fat pad as the knee bends from extension to flexion through different angles. This multi-angle analysis provides a finer description of the intermediate deformations compared to earlier work, where only a pair of images (full extension and flexion) was analyzed. },
}

@inproceedings{Johnson,
  year = { 2006 },
  volume = { 48 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868696757{\&}partnerID=40{\&}md5=4571d3f07f7e58d431d9e91206610690 },
  type = { Conference Proceedings },
  title = { Biomedical computing and visualization },
  pages = { 3--10 },
  keywords = { Biomedical computing,Imaging,Problem solving environment,Visualization },
  issn = { 14451336 },
  isbn = { 1920682309 },
  doi = { 10.1109/pact.2003.1238017 },
  booktitle = { Conferences in Research and Practice in Information Technology Series },
  author = { Johnson and Weinstein },
  abstract = { Computers have changed the way we live, work, and even recreate. Now, they are transforming how we think about and treat human disease. In particular, advanced techniques in biomedical computing, imaging, and visualization are changing the face of biology and medicine in both research and clinical practice. The goals of biomedical computing, imaging and visualization are multifaceted. While some images and visualizations facilitate diagnosis, others help physicians plan surgery. Biomedical simulations can help to acquire a better understanding of human physiology. Still other biomedical computing and visualization techniques are used for medical training. Within biomedical research, new computational technologies allow us to "see" into and understand our bodies with unprecedented depth and detail. As a result of these advances, biomedical computing and visualization will help produce exciting new biomedical scientific discoveries and clinical treatments. In this paper, we give an overview of the computational science pipeline for an application in neuroscience and present associated research results in medical imaging, modeling, simulation, and visualization. Copyright {\textcopyright} 2006, Australian Computer Society, Inc. },
}

@article{Shelton2005,
  year = { 2005 },
  volume = { 9 },
  title = { Teaching medical image analysis with the Insight Toolkit },
  pages = { 605--611 },
  number = { 6 },
  keywords = { Image analysis,Insight Toolkit,Teaching },
  journal = { Medical Image Analysis },
  issn = { 13618415 },
  doi = { 10.1016/j.media.2005.04.011 },
  author = { Shelton and Stetten and Aylward and Ib{\'{a}}{\~{n}}ez and Cois and Stewart },
  abstract = { We present several case studies which examine the role that the Insight Toolkit (ITK) played in three medical image analysis courses and several conference tutorials. These courses represent the first use of ITK in a teaching environment, and we believe that a discussion of the teaching approach in each case and the benefits and challenges of ITK will be useful to future medical image analysis course development. ITK was found to provide significant value in a classroom setting since it provides both working "canned" algorithms, including some recently developed methods that are unavailable elsewhere, as well as a framework for developing new techniques and applications. Several areas of difficulty, particularly in regards to code complexity and advanced object-oriented design techniques, have been identified which may make the learning curve of ITK somewhat more complex than a language such as Matlab™. {\textcopyright} 2005 Elsevier B.V. All rights reserved. },
}

@article{Wang2005,
  year = { 2005 },
  url = { http://insight-journal.org/dspace/handle/1926/43 },
  title = { Real-time ultrasound image analysis for the insight toolkit },
  pages = { 1--5 },
  journal = { Proc MICCAI },
  author = { Wang and Chang and Stetten },
  abstract = { We have successfully created a software environment in which ultrasound data can be manipulated by, ITK (the Insight Tool-Kit), in real-time. We were able to access each frame generated within the resident computer of a TerasonTM Ultrasound Machine, convert it into the ITK image format, and demonstrate the concurrent operation of ITK on the same computer by writing the images to an external hard drive. At a rate of 10 frames per second, 512 by 512 pixel grayscale frames were written by ITK methods to the external hard drive through USB 2.0 while the ultrasound scan was occurring without thrashing or delay in system performance. This simple exercise demonstrates the potential of ITK in processing ultrasound images in real-time in addition to the more traditional off-line processing. },
}

@article{2005,
  year = { 2005 },
  volume = { 9 },
  url = { {\%}3CGo to },
  type = { Journal Article },
  title = { Efficient multi-modal dense field non-rigid registration: Alignment of histological and section images },
  pages = { 538--546 },
  number = { 6 },
  keywords = { Elastic regularization,ITK,Laryngectomy,Mutual information,Non-rigid registration },
  journal = { Medical Image Analysis },
  issn = { 13618415 },
  doi = { 10.1016/j.media.2005.04.003 },
  author = { Bois d'Aische and Craene and Geets and Gregoire and Macq and Warfield },
  abstract = { We describe a new algorithm for non-rigid registration capable of estimating a constrained dense displacement field from multi-modal image data. We applied this algorithm to capture non-rigid deformation between digital images of histological slides and digital flat-bed scanned images of cryotomed sections of the larynx, and carried out validation experiments to measure the effectiveness of the algorithm. The implementation was carried out by extending the open-source Insight ToolKit software. In diagnostic imaging of cancer of the larynx, imaging modalities sensitive to both anatomy (such as MRI and CT) and function (PET) are valuable. However, these modalities differ in their capability to discriminate the margins of tumor. Gold standard tumor margins can be obtained from histological images from cryotomed sections of the larynx. Unfortunately, the process of freezing, fixation, cryotoming and staining the tissue to create histological images introduces non-rigid deformations and significantcontrast changes. We demonstrate that the non-rigid registration algorithm we present is able to capture these deformations and the algorithm allows us to align histological images with scanned images of the larynx. Our non-rigid registration algorithm constructs a deformation field to warp one image onto another. The algorithm measures image similarity using a mutual information similarity criterion, and avoids spurious deformations due to noise by constraining the estimated deformation field with a linear elastic regularization term. The finite element method is used to represent the deformation field, and our implementation enables us to assign inhomogeneous material characteristics so that hard regions resist internal deformation whereas soft regions are more pliant. A gradient descent optimization strategy is used and this has enabled rapid and accurate convergence to the desired estimate of the deformation field. A further acceleration in speed without cost of accuracy is achieved by using an adaptive mesh refinement strategy. {\textcopyright} 2005 Elsevier B.V. All rights reserved. },
}

@incollection{Jackowski2005,
  year = { 2005 },
  volume = { 3750 LNCS },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-33744812176{\&}doi=10.1007{\%}2F11566489{\_}88{\&}partnerID=40{\&}md5=1a20beb704d367c3bb74ae1934b0a84d },
  type = { Serial },
  title = { A computer-aided design system for revision of segmentation errors },
  pages = { 717--724 },
  issn = { 03029743 },
  isbn = { 3540293264 },
  doi = { 10.1007/11566489_88 },
  booktitle = { Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
  author = { Jackowski and Goshtasby },
  abstract = { Automatic image segmentation methods often involve errors, requiring the assistance of the user to correct them. In this paper, a computer-aided design system is introduced for correcting such errors. The proposed system approximates each 3-D region by a parametric surface. Region voxels are first parametrized spherically using a coarseto-fine subdivision method. By using the voxel positions and their parameter coordinates, control points of a rational Gaussian surface are determined through a least-squares method to approximate the region. Finally, this surface is overlaid with the volumetric image and by locally pulling or pushing it with the mouse while viewing image information, the surface is revised as needed. Typically, a few minutes are sufficient to correct errors in a region. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005. },
}

@inproceedings{Mueller,
  year = { 2005 },
  volume = { 2005 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846990428{\&}doi=10.1109{\%}2FDICTA.2005.1578115{\&}partnerID=40{\&}md5=d567baa5b241ec2cbf2f4ffd4a343b46 },
  type = { Conference Proceedings },
  title = { Improved direct volume visualisation of the coronary arteries using fused segmented regions },
  pages = { 110--117 },
  isbn = { 0769524672 },
  doi = { 10.1109/DICTA.2005.1578115 },
  booktitle = { Proceedings of the Digital Imaging Computing: Techniques and Applications, DICTA 2005 },
  author = { Mueller and Maeder and O'Shea },
  abstract = { Coronary heart disease was the single largest cause of sudden death in Australia in 2002. Computed tomography angiography (CTA) provides high resolution, high contrast images of the thoracic cavity, and as such has emerged as the imaging modality of choice for diagnosing and planning treatment for coronary heart disease. However, radiologists and cardiac surgeons require tools to easily identify possible stenosis (narrowed or constricted coronary vessels) in such CTA datasets. We present a method which allows users to interactively visualise a specific three-dimensional region of interest (ROI). In our example, segmentation methods are applied to isolate the coronary vessels, which in turn are visually enhanced using various perceptual cues. The segmentation is achieved using a combination of thresholding, region-growing, and morphological operations. The perceptual enhancement is realized by fusing direct volume rendered images using weighting factors determined by the segmented regions. The user can allow for the easy dissemination of relevant information by adjusting 'transfer functions' to control the degree of ROI enhancement. This approach requires only roughly segmented regions of interest, and allows for the 3D visualisation of calcifications within vessels. This proposed method has significant potential for helping to facilitate the efficient treatment for coronary heart disease. Furthermore, it can be implemented at interactive framerates on comparatively cheap, desktop computing hardware making it readily accessible. {\textcopyright} 2005 IEEE. },
}

@misc{Ibanez2005,
  year = { 2005 },
  volume = { Second },
  url = { http://www.itk.org/ItkSoftwareGuide.pdf },
  title = { The ITK Software Guide },
  pmid = { 1000070720 },
  pages = { 804 },
  number = { May },
  issn = { 10445323 },
  isbn = { 1930934157 },
  doi = { 1�930934-15�7 },
  booktitle = { The ITK Software Guide },
  author = { Ibanez and Schroeder and Ng and Cates },
  abstract = { Everything you need to install, use, and extend the Insight Segmentation and Registration Toolikit ITK. Includes detailed examples, installation procedures, and system overview for ITK version 2.4. (The included examples are taken directly from the ITK source code repository and are designed to demonstrate the essential features of the software.) The book comes with a CD-ROM that contains a complete hyperlinked version of the book plus ITK source code, data, Windows binaries, and extensive class documentation. Also includes CMake binaries for managing the ITK build process on a variety of compiler and operating system configurations. },
}

@book{Ibanez2003,
  year = { 2003 },
  volume = { 5 },
  url = { http://www.amazon.com/dp/1930934106 },
  title = { The ITK Software Guide: The Insight Segmentation and Registration Toolkit },
  pages = { 539 },
  number = { 4 },
  issn = { 1089-7771 },
  isbn = { 1930934106 },
  doi = { 10.1109/4233.966107 },
  booktitle = { IEEE Transactions on Information Technology in Biomedicine },
  author = { Ibanez and Schroeder and Ng and Cates },
  abstract = { Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example. },
}

@incollection{Stredney2003,
  year = { 2003 },
  volume = { 94 },
  url = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-6344254574{\&}doi=10.3233{\%}2F978-1-60750-938-7-329{\&}partnerID=40{\&}md5=a00d000d033135abebb47756881fd5ff },
  type = { Serial },
  title = { Facilitating real-time volume interaction },
  pages = { 329--335 },
  issn = { 18798365 },
  isbn = { 1586033204 },
  doi = { 10.3233/978-1-60750-938-7-329 },
  booktitle = { Studies in Health Technology and Informatics },
  author = { Stredney and Bryan and Sessanna and Kerwin },
  abstract = { We report on efforts to provide high-level intuitive tools that exploit commodity-based computing to facilitate real-time and distributed interactions with volumetric data. These efforts include an open source volume-rendering library, a portable volume visualization application framework, and parallel volume-rendering exploiting commodity-based hardware. We present our design and implementations, as well as examples of some of the various groups currently utilizing these tools, and discuss the tradeoffs of our developments versus existing techniques. },
}

@inproceedings{Ibanez2002,
  year = { 2002 },
  volume = { 2002-Janua },
  title = { Registration patterns: The generic framework for image registration of the insight toolkit },
  pages = { 345--348 },
  issn = { 19458452 },
  isbn = { 078037584X },
  doi = { 10.1109/ISBI.2002.1029264 },
  booktitle = { Proceedings - International Symposium on Biomedical Imaging },
  author = { Ib{\'{a}}{\~{n}}ez and Ng and Gee and Aylward },
  abstract = { This paper describes the design and implementation of the generic framework for image registration contained in the National Library of Medicine NLM/NIH Segmentation and Registration Toolkit (ITK). The problem of image registration has been modeled here as a structure of pluggable components that can be easily interchanged. The rationale behind the framework is presented in this paper both from the image processing and software engineering points of view. ITK is an open source project that provides a platform for developing image processing and analysis applications. State of the art practices of software engineering have been used for the design, implementation and testing of the toolkit The source code can be downloaded free of charge and used in academic and commercial applications. },
}