J. Ashburner and K. J. Friston, Unified segmentation, NeuroImage, vol.26, issue.3, pp.839-851, 2005.
DOI : 10.1016/j.neuroimage.2005.02.018

S. Bauer, L. P. Nolte, and M. Reyes, Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization, pp.354-361, 2011.
DOI : 10.1007/978-3-540-85988-8_9

F. F. Berendsen, U. A. Van-der-heide, T. R. Langerak, A. N. Kotte, and J. P. Pluim, Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR, Computer Vision and Image Understanding, vol.117, issue.9, pp.1119-1127, 2013.
DOI : 10.1016/j.cviu.2012.12.006

Y. Boykov and G. Funka-lea, Graph Cuts and Efficient N-D Image Segmentation, International Journal of Computer Vision, vol.18, issue.9, pp.109-131, 2006.
DOI : 10.1007/s11263-006-7934-5

M. Brett, A. P. Leff, C. Rorden, and J. Ashburner, Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking, NeuroImage, vol.14, issue.2, pp.486-500, 2001.
DOI : 10.1006/nimg.2001.0845

N. Chitphakdithai and J. S. Duncan, Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images, pp.367-374, 2010.
DOI : 10.1007/978-3-642-15705-9_45

M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtagh et al., Automatic tumor segmentation using knowledge-based techniques, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.187-201, 1998.
DOI : 10.1109/42.700731

D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, and A. Murtha, 3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409130

J. J. Corso, E. Sharon, S. Dube, S. El-saden, U. Sinha et al., Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification, IEEE Transactions on Medical Imaging, vol.27, issue.5, pp.629-640, 2008.
DOI : 10.1109/TMI.2007.912817

M. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J. Villemure et al., Atlas-based segmentation of pathological mr brain images using a model of lesion growth, IEEE Transactions on Medical Imaging, vol.23, pp.1301-1314, 2004.

L. M. Fletcher-heath, L. O. Hall, D. B. Goldgof, and F. R. Murtagh, Automatic segmentation of non-enhancing brain tumors in magnetic resonance images, Artificial Intelligence in Medicine, vol.21, issue.1-3, pp.43-63, 2001.
DOI : 10.1016/S0933-3657(00)00073-7

V. S. Fonov, A. C. Evans, R. C. Mckinstry, C. R. Almli, and D. L. Collins, Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, NeuroImage, vol.47, issue.0909, pp.1053-811970884, 2009.
DOI : 10.1016/S1053-8119(09)70884-5

J. Friedman, T. Hastie, and R. Tibshirani, Additive Logistic Regression: a Statistical View of Boosting, The Annals of Statistics, vol.38, pp.337-407, 2000.

C. García and J. A. Moreno, Kernel Based Method for Segmentation and Modeling of Magnetic Resonance Images, pp.636-645, 2004.
DOI : 10.1007/978-3-540-30498-2_64

D. T. Gering, W. E. Grimson, and R. Kikinis, Recognizing Deviations from Normalcy for Brain Tumor Segmentation, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.388-395, 2002.
DOI : 10.1007/3-540-45786-0_48

B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N. Paragios, Dense image registration through MRFs and efficient linear programming???, Medical Image Analysis, vol.12, issue.6, pp.731-741, 2008.
DOI : 10.1016/j.media.2008.03.006

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

B. Glocker, A. Sotiras, N. Komodakis, and N. Paragios, Deformable medical image registration: Setting the state of the art with discrete methods*. Annual review of biomedical engineering 13, pp.219-244, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00858380

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, Peters: MICCAI 2011, Part II, pp.532-540, 2011.
DOI : 10.1109/42.790458

L. Görlitz, B. Menze, M. A. Weber, B. Kelm, and F. Hamprecht, Semisupervised tumor detection in magnetic resonance spectroscopic images using discriminative random fields, in: Pattern Recognition, pp.224-233, 2007.

S. Ho, E. Bullitt, and G. Gerig, Level-set evolution with region competition: automatic 3-D segmentation of brain tumors, Object recognition supported by user interaction for service robots, pp.532-535, 2002.
DOI : 10.1109/ICPR.2002.1044788

M. Kaus, S. Warfield, A. Nabavi, P. Black, F. Jolesz et al., Automated Segmentation of MR Images of Brain Tumors, Radiology, vol.218, issue.2, pp.586-591, 2001.
DOI : 10.1148/radiology.218.2.r01fe44586

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, Efficient training for pairwise or higher order CRFs via dual decomposition, CVPR 2011, pp.1841-1848, 2011.
DOI : 10.1109/CVPR.2011.5995375

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

S. K. Kyriacou, C. Davatzikos, S. J. Zinreich, and R. N. Bryan, Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI], IEEE Transactions on Medical Imaging, vol.18, issue.7, pp.580-592, 1999.
DOI : 10.1109/42.790458

C. H. Lee, S. Wang, A. Murtha, M. R. Brown, and R. Greiner, Segmenting Brain Tumors Using Pseudo???Conditional Random Fields, pp.359-366, 2008.
DOI : 10.1007/978-3-540-85988-8_43

D. Mahapatra and Y. Sun, Integrating Segmentation Information for Improved MRF-Based Elastic Image Registration, IEEE Transactions on Image Processing, vol.21, issue.1, pp.170-183, 2012.
DOI : 10.1109/TIP.2011.2162738

B. S. Manjunath and W. Y. Ma, Texture features for browsing and retrieval of image data. Pattern Analysis and Machine Intelligence, IEEE Transactions, vol.18, pp.837-842, 1996.

B. H. Menze, K. Van-leemput, D. Lashkari, M. A. Weber, N. Ayache et al., A Generative Model for Brain Tumor Segmentation in Multi-Modal Images, pp.151-159, 2010.
DOI : 10.1007/978-3-642-15745-5_19

URL : https://hal.archives-ouvertes.fr/hal-00813776

A. Mohamed, E. I. Zacharaki, D. Shen, and C. Davatzikos, Deformable registration of brain tumor images via a statistical model of tumorinduced deformation. Medical image analysis 10, pp.752-763, 2006.

Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, Dramms: Deformable registration via attribute matching and mutual-saliency weighting. Medical image analysis 15, pp.622-639, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00856309

S. Ourselin, A. Roche, S. Prima, and N. Ayache, Block matching: A general framework to improve robustness of rigid registration of medical images , in: Medical Image Computing and Computer-Assisted Intervention? MICCAI, pp.557-566, 2000.

J. Pallud, L. Taillandier, L. Capelle, D. Fontaine, M. Peyre et al., Quantitative Morphological Magnetic Resonance Imaging Follow-up of Low-Grade Glioma, Neurosurgery, vol.71, issue.3, pp.729-768, 2012.
DOI : 10.1227/NEU.0b013e31826213de

S. Parisot, H. Duffau, S. Chemouny, and N. Paragios, Graph Based Spatial Position Mapping of Low-Grade Gliomas, Peters: MICCAI 2011, Part II, pp.508-515, 2011.
DOI : 10.1007/978-3-642-04268-3_83

URL : https://hal.archives-ouvertes.fr/hal-00775865

S. Parisot, H. Duffau, S. Chemouny, and N. Paragios, Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images, MICCAI, pp.651-658, 2012.
DOI : 10.1007/978-3-642-33418-4_80

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

K. M. Pohl, J. Fisher, W. E. Grimson, R. Kikinis, and W. M. Wells, A Bayesian model for joint segmentation and registration, NeuroImage, vol.31, issue.1, pp.228-239, 2006.
DOI : 10.1016/j.neuroimage.2005.11.044

M. Prastawa, E. Bullit, N. Moon, K. Leemput, and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors1, Academic Radiology, vol.10, issue.12, pp.1341-1348, 2003.
DOI : 10.1016/S1076-6332(03)00506-3

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

P. Risholm, E. Samset, I. F. Talos, and W. Wells, A Non-rigid Registration Framework That Accommodates Resection and Retraction, pp.447-458, 2009.
DOI : 10.1109/TIP.2005.846026

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, Medical Image Computing and Computer-Assisted Intervention?MICCAI, pp.659-666, 2012.
DOI : 10.1007/978-3-642-33418-4_81

R. Soffietti, B. G. Baumert, L. Bello, A. Von-deimling, H. Duffau et al., Guidelines on management of low-grade gliomas: report of an EFNS-EANO* Task Force, European Journal of Neurology, vol.27, issue.9, pp.1124-1157, 2010.
DOI : 10.1111/j.1468-1331.2010.03151.x

A. Sotiras, C. Davatzikos, and N. Paragios, Deformable Medical Image Registration: A Survey, IEEE Transactions on Medical Imaging, vol.32, issue.7, pp.1153-1190, 2013.
DOI : 10.1109/TMI.2013.2265603

URL : https://hal.archives-ouvertes.fr/hal-00684715

R. Stefanescu, O. Commowick, G. Malandain, P. Y. Bondiau, N. Ayache et al., Non-rigid Atlas to Subject Registration with Pathologies for Conformal Brain Radiotherapy, pp.704-711, 2004.
DOI : 10.1007/978-3-540-30135-6_86

S. Taheri, S. Ong, and V. Chong, Level-set segmentation of brain tumors using a threshold-based speed function, Image and Vision Computing, vol.28, issue.1, pp.26-37, 2010.
DOI : 10.1016/j.imavis.2009.04.005

J. P. Thirion, Image matching as a diffusion process: an analogy with maxwell's demons. Medical image analysis 2, pp.243-260, 1998.

R. Verma, E. I. Zacharaki, Y. Ou, H. Cai, S. Chawla et al., Multi-parametric tissue characterization of brain neoplasms and their recurrence using pattern classification of mr images Academic radiology 15, p.966, 2008.

C. Wang, N. Komodakis, and N. Paragios, Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey, Computer Vision and Image Understanding, vol.117, issue.11, pp.1610-1627, 2013.
DOI : 10.1016/j.cviu.2013.07.004

URL : https://hal.archives-ouvertes.fr/hal-00858390

M. Wels, G. Carneiro, A. Aplas, M. Huber, J. Hornegger et al., A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI, pp.67-75, 2008.
DOI : 10.1007/978-3-540-85988-8_9

P. P. Wyatt and J. A. Noble, MAP MRF joint segmentation and registration of medical images, Medical Image Analysis, vol.7, issue.4, pp.539-52, 2003.
DOI : 10.1016/S1361-8415(03)00067-7

X. Xuan and Q. Liao, Statistical Structure Analysis in MRI Brain Tumor Segmentation, Fourth International Conference on Image and Graphics (ICIG 2007), pp.421-426, 2007.
DOI : 10.1109/ICIG.2007.181

A. Yezzi, L. Zollei, and T. Kapur, A variational framework for integrating segmentation and registration through active contours, Medical Image Analysis, vol.7, issue.2, pp.171-185, 2003.
DOI : 10.1016/S1361-8415(03)00004-5

E. Zacharaki, D. Shen, S. Lee, and C. Davatzikos, ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images, IEEE Transactions on Medical Imaging, vol.27, issue.8, pp.1003-1017, 2008.
DOI : 10.1109/TMI.2008.916954

Y. Zhan and D. Shen, Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method, pp.688-696, 2003.
DOI : 10.1007/978-3-540-39899-8_84

J. Zhang, K. K. Ma, M. H. Er, and V. Chong, Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine, International Workshop on Advanced Image Technology, pp.207-211, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00548532

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR, Medical Image Computing and Computer-Assisted Intervention?MICCAI, pp.369-376, 2012.
DOI : 10.1007/978-3-642-33454-2_46

D. Zikic, B. Glocker, O. Kutter, M. Groher, N. Komodakis et al., Linear intensity-based image registration by Markov random fields and discrete optimization, Medical Image Analysis, vol.14, issue.4, pp.550-562, 2010.
DOI : 10.1016/j.media.2010.04.003

URL : https://hal.archives-ouvertes.fr/hal-00856077