M. Bach-cuadra, M. De-craene, V. Duay, B. Macq, C. Pollo et al., Dense deformation field estimation for atlasbased segmentation of pathological MR brain images. Computer methods and programs in biomedicine, pp.66-75, 2006.

Y. Choi and S. Lee, Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions, Graphical Models, vol.62, issue.6, 2000.
DOI : 10.1006/gmod.2000.0531

A. Doucet, N. De-freitas, K. Murphy, and S. Russell, Raoblackwellised particle filtering for dynamic bayesian networks, Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI- 00), pp.176-183, 2000.

J. Friedman, T. Hastie, and R. Tibshirani, Additive Logistic Regression: a Statistical View of Boosting. The Annals of Statistics, pp.337-407, 2000.

B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab, Optical flow estimation with uncertainties through dynamic MRFs, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587562

A. Gooya, K. M. Pohl, M. Bilello, G. Biros, and C. Davatzikos, Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model, 2011.
DOI : 10.1109/42.790458

J. E. Iglesias, M. R. Sabuncu, and K. V. Leemput, Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfield Volumetry, MICCAI, pp.50-57, 2012.
DOI : 10.1007/978-3-642-33454-2_7

P. Kohli and P. H. Torr, Measuring uncertainty in graph cut solutions, Computer Vision and Image Understanding, vol.112, issue.1, pp.30-38, 2008.
DOI : 10.1016/j.cviu.2008.07.002

N. Komodakis, G. Tziritas, and N. Paragios, Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies, Computer Vision and Image Understanding, vol.112, issue.1, pp.14-29, 2008.
DOI : 10.1016/j.cviu.2008.06.007

URL : https://hal.archives-ouvertes.fr/hal-00918699

B. S. Manjunath and W. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.8, pp.837-842, 1996.
DOI : 10.1109/34.531803

S. Parisot, H. Duffau, S. Chemouny, and N. Paragios, Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images, MICCAI, pp.651-658, 2007.
DOI : 10.1007/978-3-642-33418-4_80

URL : https://hal.archives-ouvertes.fr/hal-00773618

M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, A brain tumor segmentation framework based on outlier detection*1, Medical Image Analysis, vol.8, issue.3, pp.275-283, 2004.
DOI : 10.1016/j.media.2004.06.007

K. Rohr, Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties, MICCAI, pp.1174-1183, 1998.
DOI : 10.1137/1.9781611970128

D. Rueckert, L. Sonoda, I. Hayes, D. Hill, M. Leach et al., Nonrigid registration using free-form deformations: application to breast MR images, IEEE Transactions on Medical Imaging, vol.18, issue.8, pp.712-721, 1999.
DOI : 10.1109/42.796284

W. Shi, X. Zhuang, L. Pizarro, W. Bai, H. Wang et al., Registration Using Sparse Free-Form Deformations, MICCAI, pp.659-666, 2012.
DOI : 10.1007/978-3-642-33418-4_81

C. V. Stewart, Y. Lee, and C. Tsai, An uncertaintydriven hybrid of intensity-based and feature-based registration with application to retinal and lung ct images, MIC- CAI, pp.870-877, 2004.

M. Taron, N. Paragios, and M. Jolly, Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.1, pp.99-113, 2009.
DOI : 10.1109/TPAMI.2008.36

URL : https://hal.archives-ouvertes.fr/hal-00712244