. .. , Software tools selected for evaluation

.. .. Results,

.. .. Discussion,

.. .. Conclusion,

, Deep CNN for patches segmentation, p.67

.. .. Results,

D. .. Conclusion,

, 2.2 Pre-segmentation using level-sets

. .. Results, , vol.80, p.75

.. .. Conclusion,

S. Y. Ababneh, J. W. Prescott, and M. N. Gurcan, Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research, Medical Image Analysis, vol.15, pp.438-448, 2011.

D. Akers, A. Sherbondy, R. Mackenzie, R. Dougherty, and B. Wandell, Exploration of the brain's white matter pathways with dynamic queries, Proceedings of the conference on Visualization'04, pp.377-384, 2004.

L. Alamo, B. J. Meyrat, J. Meuwly, R. A. Meuli, and F. Gudinchet, Anorectal malformations: finding the pathway out of the labyrinth, Radiographics, vol.33, pp.491-512, 2013.

S. G. Antunes, J. S. Silva, and J. B. Santos, A level set segmentation method of the four heart cavities in pediatric ultrasound images, International Conference Image Analysis and Recognition, pp.99-107, 2010.

S. Arezoomand, W. Lee, K. S. Rakhra, and P. E. Beaulé, A 3D active model framework for segmentation of proximal femur in MR images, International Journal of Computer Assisted Radiology and Surgery, vol.10, pp.55-66, 2015.

S. Banik, R. M. Rangayyan, and G. S. Boag, Delineation of the pelvic girdle in computed tomographic images, IEEE Canadian Conference on Electrical and Computer Engineering, pp.179-182, 2008.

S. Banik, R. M. Rangayyan, and G. S. Boag, Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images, Journal of Digital Imaging, vol.23, pp.301-322, 2010.

Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen et al., Chest pathology detection using deep learning with nonmedical training, IEEE 12th International Symposium on Biomedical Imaging (ISBI) (2015), pp.294-297

I. Bloch, Fuzzy spatial relationships for image processing and interpretation: a review, Image and Vision Computing, vol.23, pp.89-110, 2005.

F. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, pp.567-585, 1989.

T. Boskamp, D. Rinck, F. Link, B. Kummerlen, G. Stamm et al., New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets, Radiographics, vol.24, pp.287-297, 2004.

P. Bourgeat, J. Fripp, P. Stanwell, S. Ramadan, and S. Ourselin, MR image segmentation of the knee bone using phase information, Medical Image Analysis, vol.11, pp.325-335, 2007.

M. S. Brown, W. C. Feng, T. R. Hall, M. F. Mcnitt-gray, and B. M. Churchill, Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume, Journal of Computer Assisted Tomography, vol.25, pp.639-648, 2001.

G. Bueno, M. Fisher, K. Burnham, J. Mills, and O. Haas, Automatic segmentation of clinical structures for RTP: Evaluation of a morphological approach, Medical Image Understanding and Analysis, pp.73-76, 2001.

J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.679-698, 1986.

J. Carballido-gamio, S. J. Belongie, and S. Majumdar, Normalized cuts in 3-D for spinal MRI segmentation, IEEE Transactions on Medical Imaging, vol.23, pp.36-44, 2004.

J. J. Cerrolaza, N. Safdar, C. A. Peters, E. Myers, J. Jago et al., Segmentation of kidney in 3D-ultrasound images using Gabor-based appearance models, IEEE 11th International Symposium on, pp.633-636, 2014.

T. F. Chan and L. A. Vese, Active contours without edges, IEEE Transactions on Image Processing, vol.10, pp.266-277, 2001.
DOI : 10.1109/83.902291
URL : http://www.math.ucla.edu/~lvese/PAPERS/IEEEIP2001.pdf

S. S. Chandra, Y. Xia, C. Engstrom, S. Crozier, R. Schwarz et al., Focused shape models for hip joint segmentation in 3D magnetic resonance images, Medical Image Analysis, vol.18, pp.567-578, 2014.

C. Chen and G. Zheng, Fully automatic segmentation of AP pelvis Xrays via random forest regression with efficient feature selection and hierarchical sparse shape composition, Computer Vision and Image Understanding, vol.126, pp.1-10, 2014.

C. Chu, J. Bai, X. Wu, and G. Zheng, MASCG: multi-atlas segmentation constrained graph method for accurate segmentation of hip CT images, Medical Image Analysis, vol.26, pp.173-184, 2015.

, Seg3D: Volumetric Image Segmentation and Visualization. Scientific Computing and Imaging Institute (SCI), 2016.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, 3D U-net: learning dense volumetric segmentation from sparse annotation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.424-432, 2016.

L. D. Cohen and I. Cohen, Finite-element methods for active contour models and balloons for 2-D and 3-D images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, pp.1131-1147, 1993.

M. J. Costa, H. Delingette, A. , and N. , Automatic segmentation of the bladder using deformable models, Biomedical Imaging: From Nano to Macro, pp.904-907, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00616055

A. Delmonte, I. Bloch, D. Hasboun, C. Mercier, J. Pallud et al., Segmentation of White Matter Tractograms Using Fuzzy Spatial Relations, Organization for Human Brain Mapping, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01744267

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A Large-Scale Hierarchical Image Database, IEEE Conference on Computer Vision and Pattern Recognition, 2009.

M. Descoteaux, L. Collins, and K. Siddiqi, A multi-scale geometric flow for segmenting vasculature in MRI, Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, pp.169-180, 2004.

M. Descoteaux, R. Deriche, T. R. Knosche, and A. Anwander, Deterministic and probabilistic tractography based on complex fibre orientation distributions, IEEE transactions on medical imaging, vol.28, pp.269-286, 2009.

C. N. Devi, A. Chandrasekharan, V. Sundararaman, A. , and Z. C. , Neonatal brain MRI segmentation: A review, Computers in Biology and Medicine, vol.64, pp.163-178, 2015.

L. R. Dice, Measures of the amount of ecologic association between species, Ecology, vol.26, pp.297-302, 1945.

R. Dietrich and H. Kangarloo, Pelvic abnormalities in children: assessment with MR imaging, Radiology, vol.163, pp.367-372, 1987.

Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang et al., 3D deeply supervised network for automated segmentation of volumetric medical images, Medical Image Analysis, vol.41, pp.40-54, 2017.

R. Drake, A. W. Vogl, and A. W. Mitchell, Gray's Anatomy for Students E-Book, 2009.

C. Duan, Z. Liang, S. Bao, H. Zhu, S. Wang et al., A coupled level set framework for bladder wall segmentation with application to MR cystography, IEEE Transactions on Medical Imaging, vol.29, pp.903-915, 2010.

J. Ehrhardt, H. Handels, W. Plotz, and S. Poppl, Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters, Methods of Information in Medicine, vol.43, pp.391-397, 2004.

X. Fang, The extruded generalized cylinder: A deformable model for object recovery, Computer Vision and Pattern Recognition, pp.174-181, 1994.

A. Fedorov, R. Beichel, J. Kalpathy-cramer, J. Finet, J. Fillion-robin et al., 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network, Magnetic Resonance Imaging, vol.30, pp.1323-1341, 2012.

V. Ferrari, M. Carbone, C. Cappelli, L. Boni, F. Melfi et al., Value of multidetector computed tomography image segmentation for preoperative planning in general surgery, Surgical Endoscopy, vol.26, issue.3, pp.616-626, 2012.

B. Fischl and . Freesurfer, NeuroImage, vol.62, pp.774-781, 2012.

G. Fouquier, J. Anquez, I. Bloch, C. Falip, A. et al., Subcutaneous adipose tissue segmentation in whole-body MRI of children, Iberoamerican Congress on Pattern Recognition, pp.97-104, 2011.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, Multiscale vessel enhancement filtering, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.130-137, 1998.

T. Gangwar, J. Calder, T. Takahashi, J. E. Bechtold, and D. Schillinger, Robust variational segmentation of 3D bone CT data with thin cartilage interfaces, Medical Image Analysis, vol.47, pp.95-110, 2018.

C. Garnier, W. Ke, and J. Dillenseger, Bladder segmentation in MRI images using active region growing model, Annual International Conference of the IEEE, pp.5702-5705, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00620224

R. Gauriau, Shape-based approaches for fast multi-organ localization and segmentation in 3D medical images, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01254550

D. Geffroy, D. Rivière, I. Denghien, N. Souedet, S. Laguitton et al., Brainvisa: a complete software platform for neuroimaging, Python in Neuroscience Workshop, 2011.

G. Gerig, O. Kubler, R. Kikinis, and F. A. Jolesz, Nonlinear anisotropic filtering of MRI data, IEEE Transactions on Medical Imaging, vol.11, pp.221-232, 1992.

B. Gilles and N. Magnenat-thalmann, Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations, Medical Image Analysis, vol.14, pp.291-302, 2010.
DOI : 10.1016/j.media.2010.01.006

E. M. Haacke, R. W. Brown, M. R. Thompson, and R. Venkatesan, Magnetic resonance imaging: physical principles and sequence design, vol.82, 1999.

D. Haak, C. Page, and T. M. Deserno, A survey of DICOM viewer software to integrate clinical research and medical imaging, Journal of Digital Imaging, vol.29, pp.206-215, 2016.

T. Heimann and H. Meinzer, Statistical shape models for 3D medical image segmentation: A review, Medical Image Analysis, vol.13, pp.543-563, 2009.

T. Heimann, B. Van-ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich et al., Comparison and evaluation of methods for liver segmentation from CT datasets, IEEE Transactions on Medical Imaging, vol.28, pp.1251-1265, 2009.

M. Hernández-hoyos, Segmentation anisotrope 3D pour la quantification en imagerie vasculaire par résonance magnétique, 2002.

T. Hida, Brownian motion, Brownian Motion, pp.44-113, 1980.

J. Hiltunen, T. Suortti, S. Arvela, M. Seppä, R. Joensuu et al., Diffusion tensor imaging and tractography of distal peripheral nerves at 3T, Clinical Neurophysiology, vol.116, pp.2315-2323, 2005.

M. Holden, A review of geometric transformations for nonrigid body registration, IEEE Transactions on Medical Imaging, vol.27, pp.111-128, 2008.

T. Huisman, Diffusion-weighted and diffusion tensor imaging of the brain, made easy, Cancer Imaging, vol.10, p.163, 2010.

I. I?gum, M. J. Benders, B. Avants, M. J. Cardoso, S. J. Counsell et al., Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge, Medical Image Analysis, vol.20, pp.135-151, 2015.

M. Jenkinson, C. F. Beckmann, T. Behrens, M. W. Woolrich, S. M. Smith et al., NeuroImage, vol.62, pp.782-790, 2012.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane et al., Efficient multiscale 3D CNN with fully connected crf for accurate brain lesion segmentation, Medical Image Analysis, vol.36, pp.61-78, 2017.

C. Kirbas and F. Quek, A review of vessel extraction techniques and algorithms, ACM Computing Surveys (CSUR), vol.36, pp.81-121, 2004.

H. Knutsson, A. , and M. , Morphons: Segmentation using elastic canvas and paint on priors, 12th IEEE International Conference on Image Processing, pp.1226-1229, 2005.

M. Kr?ah, G. Székely, and R. Blanc, Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior, Biomedical Imaging: From Nano to Macro, pp.2087-2090, 2011.

J. Kullberg, A. Karlsson, E. Stokland, P. Svensson, and J. Dahlgren, Adipose tissue distribution in children: Automated quantification using water and fat MRI, Journal of Magnetic Resonance Imaging, vol.32, pp.204-210, 2010.

H. Lamecker, M. Seebaß, H. Hege, and P. Deuflhard, A 3D statistical shape model of the pelvic bone for segmentation, Medical Imaging, vol.5370, pp.1341-1351, 2004.

M. W. Law, C. , and A. C. , Efficient implementation for spherical flux computation and its application to vascular segmentation, IEEE Transactions on Image Processing, vol.18, pp.596-612, 2009.

N. Lay, N. Birkbeck, J. Zhang, and S. K. Zhou, Rapid multiorgan segmentation using context integration and discriminative models, Information Processing in Medical Imaging, 2013.

S. Gee, K. M. Joshi, W. M. Pohl, L. Wells, and . Zöllei, , pp.450-462

L. Bihan, D. Mangin, J. Poupon, C. Clark, C. A. Pappata et al., Diffusion tensor imaging: concepts and applications, Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol.13, pp.534-546, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00349820

D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-lea, A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes, Medical Image Analysis, vol.13, pp.819-845, 2009.

B. Li and S. T. Acton, Active contour external force using vector field convolution for image segmentation, IEEE transactions on image processing, vol.16, pp.2096-2106, 2007.

W. Liao, T. M. Deserno, and K. Spitzer, Evaluation of free nondiagnostic DICOM software tools, Proceedings of SPIE, vol.6919, pp.691903-691903, 2008.

Q. Lin, Enhancement, detection, and visualization of 3D volume data, 2001.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi et al., A survey on deep learning in medical image analysis, Medical Image Analysis, vol.42, pp.60-88, 2017.

L. M. Lorigo, O. Faugeras, W. E. Grimson, R. Keriven, and R. Kikinis, Segmentation of bone in clinical knee MRI using texture-based geodesic active contours, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.1195-1204, 1998.

L. M. Lorigo, O. D. Faugeras, W. E. Grimson, R. Keriven, R. Kikinis et al., Curves: Curve evolution for vessel segmentation, Medical Image Analysis, vol.5, pp.195-206, 2001.

Z. Ma, R. N. Jorge, T. Mascarenhas, and J. M. Tavares, Novel approach to segment the inner and outer boundaries of the bladder wall in T2-weighted magnetic resonance images, Annals of Biomedical Engineering, vol.39, pp.2287-2297, 2011.

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Deep retinal image understanding, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.140-148, 2016.
DOI : 10.1007/978-3-319-46723-8_17
URL : http://arxiv.org/pdf/1609.01103

J. S. Matsumoto, J. M. Morris, T. A. Foley, E. E. Williamson, S. Leng et al., Three-dimensional physical modeling: applications and experience at Mayo Clinic, Radiographics, vol.35, pp.1989-2006, 2015.

M. Mazonakis, J. Damilakis, H. Varveris, P. Prassopoulos, and N. Gourtsoyiannis, Image segmentation in treatment planning for prostate cancer using the region growing technique, The British Journal of Radiology, vol.74, pp.243-249, 2001.

M. J. Mcauliffe, F. M. Lalonde, D. Mcgarry, W. Gandler, K. Csaky et al., Medical image processing, analysis and visualization in clinical research, 14th IEEE Symposium on ComputerBased Medical Systems (CBMS), pp.381-386, 2001.

T. Mcinerney and D. Terzopoulos, T-snakes: Topology adaptive snakes, Medical Image Analysis, vol.4, pp.73-91, 2000.

C. S. Mendoza, X. Kang, N. Safdar, E. Myers, A. D. Martin et al., Automatic analysis of pediatric renal ultrasound using shape, anatomical and image acquisition priors, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.259-266, 2013.

F. Milletari, N. Navab, and S. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation, Fourth International Conference on, pp.565-571, 2016.

J. Montagnat, Deformable modelling for 3D and 4D medical image segmentation, 1999.

S. Mori and P. C. Van-zijl, NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo, vol.15, pp.468-480, 2002.

M. M. Nillesen, R. G. Lopata, I. H. Gerrits, L. Kapusta, H. J. Huisman et al., Segmentation of the heart muscle in 3-D pediatric echocardiographic images, Ultrasound in Medicine & Biology, vol.33, pp.1453-1462, 2007.

M. M. Nillesen, R. G. Lopata, H. Huisman, J. M. Thijssen, L. Kapusta et al., Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data, Ultrasound in Medicine & Biology, vol.37, pp.1409-1420, 2011.

L. J. O'donnell and C. Westin, Automatic tractography segmentation using a high-dimensional white matter atlas, IEEE Transactions on Medical Imaging, vol.26, pp.1562-1575, 2007.

F. Oliveira and J. M. Tavares, Medical image registration: a review, Computer Methods in Biomechanics and Biomedical Engineering, vol.17, pp.73-93, 2014.

S. J. Pan, Y. , and Q. , A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, pp.1345-1359, 2010.

S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, Bilateral filtering: Theory and applications, Foundations and Trends in Computer Graphics and Vision, vol.4, pp.1-73, 2009.

D. Pasquier, T. Lacornerie, M. Vermandel, J. Rousseau, E. Lartigau et al., Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy, International Journal of Radiation Oncology* Biology* Physics, vol.68, pp.592-600, 2007.

P. Perona, M. , and J. , Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, pp.629-639, 1990.

J. Pettersson, H. Knutsson, and M. Borga, Automatic hip bone segmentation using non-rigid registration, IEEE 18th International Conference on Pattern Recognition (ICPR'06, vol.3, pp.946-949, 2006.

R. Phellan and N. D. Forkert, Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation, Medical Physics, vol.44, pp.5901-5915, 2017.

G. L. Presti, M. Carbone, D. Ciriaci, D. Aramini, M. Ferrari et al., Assessment of DICOM viewers capable of loading patient-specific 3D models obtained by different segmentation platforms in the operating room, Journal of Digital Imaging, vol.28, pp.518-527, 2015.

D. Rivière, D. Geffroy, I. Denghien, N. Souedet, and Y. Cointepas, Anatomist: a python framework for interactive 3D visualization of neuroimaging data, Python in Neuroscience Workshop, 2011.

M. Rochery, I. H. Jermyn, and J. Zerubia, New higher-order active contour energies for network extraction, Image Processing, 2005. ICIP 2005. IEEE International Conference on, vol.2, p.822, 2005.

K. Rohr, H. S. Stiehl, R. Sprengel, T. M. Buzug, J. Weese et al., Landmark-based elastic registration using approximating thin-plate splines, IEEE Transactions on Medical Imaging, vol.20, pp.526-534, 2001.

A. Rosset, L. Spadola, and O. Ratib, OsiriX: an open-source software for navigating in multidimensional DICOM images, Journal of Digital Imaging, vol.17, pp.205-216, 2004.

Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig et al., 3d multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, CVRMed-MRCAS'97, pp.213-222, 1997.

J. Schmid and N. Magnenat-thalmann, MRI bone segmentation using deformable models and shape priors, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol.5241, pp.119-126, 2008.

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, NIH image to ImageJ: 25 years of image analysis, Nature Methods, vol.9, pp.671-675, 2012.

H. Seim, D. Kainmueller, M. Heller, H. Lamecker, S. Zachow et al., Automatic segmentation of the pelvic bones from CT data based on a statistical shape model, Eurographics Workshop on Visual Computing for Biomedicine, pp.93-100, 2008.

H. Seim, D. Kainmueller, H. Lamecker, M. Bindernagel, J. Malinowski et al., Model-based auto-segmentation of knee bones and cartilage in MRI data, Medical Image Analysis for the Clinic: A Grand Challenge, 2010.

H. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu et al., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Transactions on Medical Imaging, vol.35, pp.1285-1298, 2016.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

F. W. Smith, The value of NMR imaging in pediatric practice: A preliminary report, Pediatric Radiology, vol.13, issue.3, pp.141-147, 1983.

Q. Song, Y. Liu, Y. Liu, P. K. Saha, M. Sonka et al., Graph search with appearance and shape information for 3-D prostate and bladder segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.172-180, 2010.

R. Sprengel, K. Rohr, and H. S. Stiehl, Thin-plate spline approximation for image registration, Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th

, Annual International Conference of the IEEE, vol.3, pp.1190-1191, 1996.

P. C. Sundgren and P. Leander, Is administration of gadoliniumbased contrast media to pregnant women and small children justified?, Journal of Magnetic Resonance Imaging, vol.34, pp.750-757, 2011.

G. T. Sung and I. S. Gill, Robotic laparoscopic surgery: a comparison of the da Vinci and Zeus systems, Urology, vol.58, pp.893-898, 2001.

R. Toledo, X. Orriols, X. Binefa, P. Radeva, J. Vitria et al., Tracking elongated structures using statistical snakes, Computer Vision and Pattern Recognition, vol.1, pp.157-162, 2000.

N. Toussaint, J. Souplet, and P. Fillard, Medical image navigation and research tool by INRIA, MICCAI, vol.7, p.280, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00616047

G. Valeri, F. A. Mazza, S. Maggi, D. Aramini, L. La-riccia et al., Open source software in a practical approach for post processing of radiologic images, La radiologia medica, vol.120, issue.3, pp.309-323, 2015.

P. K. Van-der-jagt, P. Dik, M. Froeling, T. C. Kwee, R. A. Nievelstein et al., Architectural configuration and microstructural properties of the sacral plexus: a diffusion tensor mri and fiber tractography study, Neuroimage, vol.62, pp.1792-1799, 2012.

V. Vapnik, The nature of statistical learning theory, Springer science & business media, 2013.

S. Vasilache and K. Najarian, Automated bone segmentation from pelvic CT images, Proc. IEEE Wkshp. on Bioinformatics and Biomedicine, pp.41-47, 2008.

A. Vasilevskiy and K. Siddiqi, Flux maximizing geometric flows, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.12, pp.1565-1578, 2002.

A. Virzì, P. Gori, C. Muller, E. Mille, Q. Peyrot et al., Segmentation of pelvic vessels in pediatric MRI using a patch based learning approach, Journées Francophones de Radiologique Diagnostique et Interventionnelle (JFR), 2018.

A. Virzì, P. Gori, C. O. Muller, E. Mille, Q. Peyrot et al., Segmentation of pelvic vessels in pediatric MRI using a patch-based deep learning approach, Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis, pp.97-106, 2018.

A. Virzì, J. B. Marret, C. O. Muller, L. Berteloot, N. Boddaert et al., A new method based on template registration and deformable models for pelvic bones semi-automatic segmentation in pediatric MRI, IEEE 14th International Symposium on Biomedical Imaging (ISBI, pp.323-326, 2017.

S. Wakana, A. Caprihan, M. M. Panzenboeck, J. H. Fallon, M. Perry et al., Reproducibility of quantitative tractography methods applied to cerebral white matter, Neuroimage, vol.36, pp.630-644, 2007.

X. Wang, W. E. Grimson, and C. Westin, Tractography segmentation using a hierarchical dirichlet processes mixture model, NeuroImage, vol.54, pp.290-302, 2011.

D. Wassermann, L. Bloy, E. Kanterakis, R. Verma, and R. Deriche, Unsupervised white matter fiber clustering and tract probability map generation: Applications of a gaussian process framework for white matter fibers, NeuroImage, vol.51, pp.228-241, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00407828

D. Wassermann, N. Makris, Y. Rathi, M. Shenton, R. Kikinis et al., The white matter query language: a novel approach for describing human white matter anatomy, Brain Structure and Function, vol.221, pp.4705-4721, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01247061

C. Westin, S. Warfield, A. Bhalerao, L. Mui, J. Richolt et al., Tensor controlled local structure enhancement of CT 134 images for bone segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.1205-1212, 1998.

Y. Xia, J. Fripp, S. S. Chandra, R. Schwarz, C. Engstrom et al., Automated bone segmentation from large field of view 3D MR images of the hip joint, Physics in Medicine & Biology, vol.58, p.7375, 2013.

C. Xu and J. L. Prince, Generalized gradient vector flow external forces for active contours, Signal Processing, vol.71, pp.131-139, 1998.

C. Xu, A. Yezzi, and J. L. Prince, A summary of geometric level-set analogues for a general class of parametric active contour and surface models, IEEE Workshop on Variational and Level Set Methods in Computer Vision, pp.104-111, 2001.

Y. Xu, T. Géraud, and I. Bloch, From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning, 23rd IEEE International Conference on Image Processing (ICIP), pp.4417-4421, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01735727

Y. Xu, B. Morel, S. Dahdouh, É. Puybareau, A. Virzì et al., The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities, Medical Image Analysis, vol.48, pp.75-94, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02104030

J. Yao, T. , and R. , Non-rigid registration and correspondence finding in medical image analysis using multiple-layer flexible mesh template matching, International Journal of Pattern Recognition and Artificial Intelligence, vol.17, pp.1145-1165, 2003.

J. Yi and J. B. Ra, A locally adaptive region growing algorithm for vascular segmentation, International Journal of Imaging Systems and Technology, vol.13, pp.208-214, 2003.

P. J. Yim, J. J. Cebral, R. Mullick, H. B. Marcos, and P. L. Choyke, Vessel surface reconstruction with a tubular deformable model, IEEE Transactions on Medical Imaging, vol.20, pp.1411-1421, 2001.

F. Yokota, T. Okada, M. Takao, N. Sugano, Y. Tada et al., Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure, International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI, vol.5762, pp.811-818, 2009.

P. Yushkevich, J. Piven, H. Cody, S. Ho, J. C. Gee et al., User-guided level set segmentation of anatomical structures with ITKsnap, NA-MIC/MICCAI Workshop on Open-Source Software, 2005.

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.1116-1128, 2006.

G. Zeng, X. Yang, J. Li, L. Yu, P. Heng et al., 3D U-net with multi-level deep supervision: Fully automatic segmentation of proximal femur in 3D MR images, Machine Learning in Medical Imaging, pp.274-282

Y. Zhang, J. Zhang, K. Oishi, A. V. Faria, H. Jiang et al., Atlasguided tract reconstruction for automated and comprehensive examination of the white matter anatomy, Neuroimage, vol.52, pp.1289-1301, 2010.

S. C. Zhu, Y. , and A. , Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, pp.884-900, 1996.

K. H. Zou, S. K. Warfield, A. Bharatha, C. M. Tempany, M. R. Kaus et al., Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports, Academic Radiology, vol.11, pp.178-189, 2004.