C. Of-excellence and T. , ANR-10-LABX-57) We also acknowledge funding from the Fonds de, Recherche Québec -Santé

R. 1. Collins and D. L. , Automatic 3-D model-based neuroanatomical segmentation, Human Brain Mapping, vol.16, issue.2, pp.190-208, 1995.
DOI : 10.1002/hbm.460030304

R. A. Heckemann, Automatic anatomical brain MRI segmentation combining label propagation and decision fusion, NeuroImage, vol.33, issue.1, pp.115-126, 2006.
DOI : 10.1016/j.neuroimage.2006.05.061

J. M. Lötjönen, Fast and robust multi-atlas segmentation of brain magnetic resonance images, NeuroImage, vol.49, issue.3, pp.2352-2365, 2010.
DOI : 10.1016/j.neuroimage.2009.10.026

D. L. Collins and J. C. Pruessner, Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion, NeuroImage, vol.52, issue.4, pp.1355-1366, 2010.
DOI : 10.1016/j.neuroimage.2010.04.193

P. Coupé, Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation, NeuroImage, vol.54, issue.2, pp.940-954, 2011.
DOI : 10.1016/j.neuroimage.2010.09.018

F. Rousseau, A Supervised Patch-Based Approach for Human Brain Labeling, IEEE Transactions on Medical Imaging, vol.30, issue.10, pp.1852-1862, 2011.
DOI : 10.1109/TMI.2011.2156806
URL : https://hal.archives-ouvertes.fr/hal-00631458

S. F. Eskildsen, BEaST: Brain extraction based on nonlocal segmentation technique, NeuroImage, vol.59, issue.3, pp.2362-2373, 2012.
DOI : 10.1016/j.neuroimage.2011.09.012
URL : https://hal.archives-ouvertes.fr/inserm-00629187

T. Tong, Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling, NeuroImage, vol.76, pp.11-23, 2013.
DOI : 10.1016/j.neuroimage.2013.02.069
URL : https://hal.archives-ouvertes.fr/hal-00806384

G. Wu, A generative probability model of joint label fusion for multi-atlas based brain segmentation, Medical Image Analysis, vol.18, issue.6, 2013.
DOI : 10.1016/j.media.2013.10.013

C. Barnes, Patchmatch: A randomized correspondence algorithm for structural image editing, Papers, vol.24, pp.1-2411, 2009.

W. Shi, Cardiac Image Super-Resolution with Global Correspondence Using Multi-Atlas PatchMatch, MICCAI 2013, pp.9-16, 2013.
DOI : 10.1007/978-3-642-40760-4_2

C. Barnes, The Generalized PatchMatch Correspondence Algorithm, LNCS, vol.6313, pp.29-43, 2010.
DOI : 10.1007/978-3-642-15558-1_3

J. Pruessner, Volumetry of Hippocampus and Amygdala with High-resolution MRI and Three-dimensional Analysis Software: Minimizing the Discrepancies between Laboratories, Cerebral Cortex, vol.10, issue.4, pp.433-442, 2000.
DOI : 10.1093/cercor/10.4.433

P. Coupé, Robust Rician noise estimation for MR images, Medical Image Analysis, vol.14, issue.4, pp.483-493, 2010.
DOI : 10.1016/j.media.2010.03.001

P. Coupé, An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images, IEEE Transactions on Medical Imaging, vol.27, issue.4, pp.425-441, 2008.
DOI : 10.1109/TMI.2007.906087

J. G. Sled, A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Transactions on Medical Imaging, vol.17, issue.1, pp.87-97, 1998.
DOI : 10.1109/42.668698

D. L. Collins, Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space, Journal of Computer Assisted Tomography, vol.18, issue.2, pp.192-205, 1994.
DOI : 10.1097/00004728-199403000-00005

J. V. Manjón, Robust MRI brain tissue parameter estimation by multistage outlier rejection, Magnetic Resonance in Medicine, vol.34, issue.4, pp.866-873, 2008.
DOI : 10.1002/mrm.21521