A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward et al., Global cancer statisticsLa situation du cancer en France en 2011-Rapport Institut National Du Cancer INCa, rapport 14 NovembreCancer in Australia: an overview Australian Institute of Health and Welfare (AIHW) & Australasian Association of Cancer Registries (AACR) Cancer series no. 37Experience with the spanner prostatic stent in patients unfit for surgery: an observational studyTechnological advances in radiotherapy for the treatment of localised prostate cancerIntensity-modulated radiotherapy (IMRT) of localized prostate cancer: a review and future perspectivesIndividualized planning target volumes for intrafraction motion during hypofractionated intensity-modulated radiotherapy boost for prostate cancer, Prostate Cancer Incidence and Mortality Worldwide70 Gy versus 80 Gy in localized prostate cancer: 5-year results of GETUG 06 randomized trial, pp.69-90, 2005.

A. L. Zietman, M. L. Desilvio, J. D. Slater, C. J. Rossi, J. et al., Comparison of Conventional-Dose vs High-Dose Conformal Radiation Therapy in Clinically Localized Adenocarcinoma of the Prostate, JAMA, vol.294, issue.10, pp.1233-1242, 2005.
DOI : 10.1001/jama.294.10.1233

V. Fonteyne, G. Villeirs, B. Speleers, W. De-neve, C. De-wagter et al., Intensity-Modulated Radiotherapy as Primary Therapy for Prostate Cancer: Report on Acute Toxicity After Dose Escalation With Simultaneous Integrated Boost to Intraprostatic Lesion, International Journal of Radiation Oncology*Biology*Physics, vol.72, issue.3, pp.799-807, 2008.
DOI : 10.1016/j.ijrobp.2008.01.040

C. Fiorino, T. Rancati, and R. Valdagni, Predictive models of toxicity in external radiotherapy, Cancer, vol.69, issue.S13, pp.3135-3175, 2009.
DOI : 10.1002/cncr.24354

R. De-crevoisier, C. Fiorino, and B. Dubray, Radioth??rapie prostatique??: pr??diction de la toxicit?? tardive ?? partir des donn??es dosim??triques, Cancer/Radioth??rapie, vol.14, issue.6-7, pp.460-468, 2010.
DOI : 10.1016/j.canrad.2010.07.225

C. F. Njeh, Tumor delineation: The weakest link in the search for accuracy in radiotherapy, Journal of Medical Physics, vol.33, issue.4, pp.136-140, 2008.
DOI : 10.4103/0971-6203.44472

G. Cazoulat, M. Lesaunier, A. Simon, P. Haigron, O. Acosta et al., De la radioth??rapie guid??e par l???image ?? la radioth??rapie guid??e par la dose, Cancer/Radioth??rapie, vol.15, issue.8, pp.691-699, 2011.
DOI : 10.1016/j.canrad.2011.05.011

T. Chen, S. Kim, J. Zhou, D. Metaxas, G. Rajagopal et al., 3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy, Med Image Comput Comput Assist Interv, vol.12, pp.43-50, 2009.
DOI : 10.1007/978-3-642-04268-3_6

O. Acosta, J. Dowling, G. Cazoulat, A. Simon, O. Salvado et al., Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy, Prognosis, and Intervention MICCAI 2010, 2010.
DOI : 10.1007/978-3-642-15989-3_6

URL : https://hal.archives-ouvertes.fr/hal-00910242

D. C. Collier, S. S. Burnett, M. Amin, S. Bilton, C. Brooks et al., Assessment of consistency in contouring of normal-tissue anatomic structures, Journal of Applied Clinical Medical Physics, vol.29, issue.1, pp.17-24, 2003.
DOI : 10.1120/jacmp.v4i1.2538

C. Fiorino, M. Reni, A. Bolognesi, G. M. Cattaneo, and R. Calandrino, Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning, Radiotherapy and Oncology, vol.47, issue.3, pp.285-292, 1998.
DOI : 10.1016/S0167-8140(98)00021-8

X. Gual-arnau, M. V. Ibanez-gual, F. Lliso, and S. Roldan, Organ contouring for prostate cancer: Interobserver and internal organ motion variability, Computerized Medical Imaging and Graphics, vol.29, issue.8, pp.639-686, 2005.
DOI : 10.1016/j.compmedimag.2005.06.002

C. Fiorino, V. Vavassori, G. Sanguineti, C. Bianchi, G. M. Cattaneo et al., Rectum contouring variability in patients treated for prostate cancer: impact on rectum dose???volume histograms and normal tissue complication probability, Radiotherapy and Oncology, vol.63, issue.3, pp.249-55, 2002.
DOI : 10.1016/S0167-8140(01)00469-8

C. Fiorino, M. Reni, A. Bolognesi, G. M. Cattaneo, and R. Calandrino, Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning, Radiotherapy and Oncology, vol.47, issue.3, pp.285-292, 1998.
DOI : 10.1016/S0167-8140(98)00021-8

P. B. Greer, J. A. Dowling, J. A. Lambert, J. Fripp, J. Parker et al., A magnetic resonance imaging-based workflow for planning radiation therapy for prostate cancer, The Medical journal of Australia, vol.194, pp.24-31, 2011.

J. A. Dowling, J. Lambert, J. Parker, O. Salvado, J. Fripp et al., An Atlas-Based Electron Density Mapping Method for Magnetic Resonance Imaging (MRI)-Alone Treatment Planning and Adaptive MRI-Based Prostate Radiation Therapy, International Journal of Radiation Oncology*Biology*Physics, vol.83, issue.1, pp.5-11, 2012.
DOI : 10.1016/j.ijrobp.2011.11.056

M. A. Costa, H. Delingette, S. B. Novellas, and N. Ayache, Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models, Med Image Comput Comput Assist Interv, vol.10, pp.252-260, 2007.
DOI : 10.1007/978-3-540-75757-3_31

URL : https://hal.archives-ouvertes.fr/inria-00616041

P. Aljabar, R. A. Heckemann, A. Hammers, J. V. Hajnal, and D. Rueckert, Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy, NeuroImage, vol.46, issue.3, pp.726-738, 2009.
DOI : 10.1016/j.neuroimage.2009.02.018

M. Wu, C. Rosano, P. Lopez-garcia, C. S. Carter, and H. J. Aizenstein, Optimum template selection for atlas-based segmentation, NeuroImage, vol.34, issue.4, pp.1612-1618, 2007.
DOI : 10.1016/j.neuroimage.2006.07.050

X. Han, M. S. Hoogeman, P. C. Levendag, L. S. Hibbard, D. N. Teguh et al., Atlas-Based Auto-segmentation of Head and Neck CT Images, Medical Image Compututing and Computer Assisted Interventions, vol.11, pp.434-475, 2008.
DOI : 10.1007/978-3-540-85990-1_52

O. Commowick, V. Gregoire, and G. Malandain, Atlas-based delineation of lymph node levels in head and neck computed tomography images, Radiotherapy and Oncology, vol.87, issue.2, pp.281-290, 2008.
DOI : 10.1016/j.radonc.2008.01.018

URL : https://hal.archives-ouvertes.fr/inria-00616080

L. Ramus, J. Thariat, P. Y. Marcy, Y. Pointreau, G. Bera et al., Outils de contourage, utilisation et construction d???atlas anatomiques??: exemples des cancers de la t??te et du cou, Cancer/Radioth??rapie, vol.14, issue.3, pp.206-218, 2010.
DOI : 10.1016/j.canrad.2010.01.005

I. Isgum, M. Staring, A. Rutten, M. Prokop, M. A. Viergever et al., Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans, IEEE Transactions on Medical Imaging, vol.28, issue.7, pp.1000-1010, 2009.
DOI : 10.1109/TMI.2008.2011480

E. M. Van-rikxoort, M. Prokop, B. De-hoop, M. A. Viergever, J. P. Pluim et al., Automatic Segmentation of Pulmonary Lobes Robust Against Incomplete Fissures, IEEE Transactions on Medical Imaging, vol.29, issue.6, pp.1286-96, 2010.
DOI : 10.1109/TMI.2010.2044799

M. G. Sanda, R. L. Dunn, J. Michalski, H. M. Sandler, L. Northouse et al., Quality of Life and Satisfaction with Outcome among Prostate-Cancer Survivors, New England Journal of Medicine, vol.358, issue.12, pp.1250-1261, 2008.
DOI : 10.1056/NEJMoa074311

J. A. Dowling, J. Fripp, S. Chandra, J. P. Pluim, J. Lambert et al., Fast Automatic Multi-atlas Segmentation of the Prostate from 3D MR Images, Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions, vol.26, pp.10-21, 2011.
DOI : 10.2307/1932409

T. S. Yoo, Insight Into Images: A K Peters, 2004.
DOI : 10.1201/b10657

D. Rueckert, A. F. Frangi, and J. A. Schnabel, Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration, IEEE Transactions on Medical Imaging, vol.22, issue.8, pp.1014-1025, 2003.
DOI : 10.1109/TMI.2003.815865

B. C. Davis, M. Foskey, J. Rosenman, L. Goyal, S. Chang et al., Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate, Med Image Comput Comput Assist Interv, vol.8, pp.442-450, 2005.
DOI : 10.1007/11566465_55

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, Diffeomorphic demons: Efficient non-parametric image registration, NeuroImage, vol.45, issue.1, pp.61-72, 2009.
DOI : 10.1016/j.neuroimage.2008.10.040

URL : https://hal.archives-ouvertes.fr/inserm-00349600

C. I. Tang, D. A. Loblaw, P. Cheung, L. Holden, G. Morton et al., Phase I/II Study of a Five-fraction Hypofractionated Accelerated Radiotherapy Treatment for Low-risk Localised Prostate Cancer: Early Results of pHART3, Clinical Oncology, vol.20, issue.10, pp.729-766, 2008.
DOI : 10.1016/j.clon.2008.08.006

C. Studholme, D. L. Hill, and D. J. Hawkes, An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition, vol.32, issue.1, pp.71-86, 1999.
DOI : 10.1016/S0031-3203(98)00091-0

M. Rubeaux, J. Nunes, L. Albera, and M. Garreau, Edgeworth-based approximation of Mutual Information for medical image registration, International Conference on Image Processing Theory, Tools and Applications, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00653334

J. P. Pluim, J. B. Maintz, and M. A. Viergever, Mutual-information-based registration of medical images: a survey, IEEE Transactions on Medical Imaging, vol.22, issue.8, pp.986-1004, 2003.
DOI : 10.1109/TMI.2003.815867

J. P. Pluim, J. B. Maintz, and M. A. Viergever, Interpolation Artefacts in Mutual Information-Based Image Registration, Computer Vision and Image Understanding, vol.77, issue.2, pp.211-232, 2000.
DOI : 10.1006/cviu.1999.0816

P. Thevenaz, T. Blu, and M. Unser, Interpolation revisited [medical images application], IEEE Transactions on Medical Imaging, vol.19, issue.7, pp.739-58, 2000.
DOI : 10.1109/42.875199

C. R. King, J. Lehmann, J. R. Adler, and J. Hai, CyberKnife Radiotherapy for Localized Prostate Cancer: Rationale and Technical Feasibility, Technology in Cancer Research & Treatment, vol.37, issue.1, pp.25-30, 2003.
DOI : 10.1177/153303460300200104

J. Parker, R. V. Kenyon, and D. E. , Comparison of Interpolating Methods for Image Resampling, IEEE Transactions on Medical Imaging, vol.2, issue.1, pp.31-40, 1983.
DOI : 10.1109/TMI.1983.4307610

G. J. Grevera and J. K. Udupa, An objective comparison of 3-D image interpolation methods, IEEE Transactions on Medical Imaging, vol.17, issue.4, pp.642-52, 1998.
DOI : 10.1109/42.730408

B. Zitova and J. Flusser, Image registration methods: a survey, Image and Vision Computing, vol.21, issue.11, pp.977-1000, 2003.
DOI : 10.1016/S0262-8856(03)00137-9

T. Rohlfing, R. Brandt, R. Menzel, and C. R. Maurer, Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains, NeuroImage, vol.21, issue.4, pp.1428-1442, 2004.
DOI : 10.1016/j.neuroimage.2003.11.010

T. Rohlfing, R. Brandt, C. R. Maurer, and R. Menzel, Bee brains, B-splines and computational democracy: generating an average shape atlas, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), pp.187-194, 2001.
DOI : 10.1109/MMBIA.2001.991733

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.8414

J. Dowling, J. Fripp, P. Freer, S. Ourselin, and O. Salvado, Automatic atlas-based segmentation of the prostate: a MICCAI 2009 Prostate Segmentation Challenge entry, Med Image Comput Comput Assist Interv, pp.17-24, 2009.

O. Acosta, A. Simon, F. Monge, F. Commandeur, C. Bassirou et al., Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy," presented at Biomedical Imaging: From Nano to Macro, IEEE International Symposium on, 2011.

O. Commowick and G. Malandain, Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation, Lecture Notes in Computer Science, vol.4792, pp.203-210, 2007.
DOI : 10.1007/978-3-540-75759-7_25

URL : https://hal.archives-ouvertes.fr/inria-00616045

A. Roche, S. Mã?â©riaux, M. Keller, and B. Thirion, Mixed-effect statistics for group analysis in fMRI: A nonparametric maximum likelihood approach, NeuroImage, vol.38, issue.3, pp.501-510, 2007.
DOI : 10.1016/j.neuroimage.2007.06.043

URL : https://hal.archives-ouvertes.fr/cea-00333625

X. Artaechevarria, A. Munoz-barrutia, and C. Ortiz-de-solorzano, Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data, IEEE Transactions on Medical Imaging, vol.28, issue.8, pp.1266-77, 2009.
DOI : 10.1109/TMI.2009.2014372

T. R. Langerak, U. A. Van-der-heide, A. N. Kotte, M. A. Viergever, M. Van-vulpen et al., Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE), IEEE Transactions on Medical Imaging, vol.29, issue.12, 2000.
DOI : 10.1109/TMI.2010.2057442

S. K. Warfield, K. H. Zou, and W. M. Wells, Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation, IEEE Transactions on Medical Imaging, vol.23, issue.7, pp.903-921, 2004.
DOI : 10.1109/TMI.2004.828354

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, Nonparametric Diffeomorphic Image Registration with the Demons Algorithm
URL : https://hal.archives-ouvertes.fr/inria-00166123

L. R. Dice, Measures of the Amount of Ecologic Association Between Species, Ecology, vol.26, issue.3, pp.297-302, 1945.
DOI : 10.2307/1932409

S. Chandra, J. Dowling, K. Shen, P. Raniga, J. Pluim et al., Patient Specific Prostate Segmentation in 3D Magnetic Resonance Images, IEEE Transactions on Medical Imaging, vol.31, 2012.

S. Martin, J. Troccaz, and V. Daanenc, Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model, Medical Physics, vol.2353, issue.8, pp.1579-1590, 2010.
DOI : 10.1118/1.3315367

URL : https://hal.archives-ouvertes.fr/hal-00456598