M. L. Goodenberger and R. B. Jenkins, Genetics of adult glioma, Cancer genetics, vol.205, pp.613-621, 2012.

S. Bauer, R. Wiest, L. Nolte, and M. Reyes, A survey of mri-based medical image analysis for brain tumor studies, Physics in medicine and biology, vol.58, p.97, 2013.

R. J. Gillies, P. E. Kinahan, and H. Hricak, Radiomics: images are more than pictures, they are data, Radiology, vol.278, pp.563-577, 2015.

B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-cramer, K. Farahani et al., The multimodal brain tumor image segmentation benchmark (brats), IEEE transactions on medical imaging, vol.34, pp.1993-2024, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00935640

S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki et al., Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features, p.170117, 2017.

M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical image analysis, vol.8, pp.275-283, 2004.

A. Gooya, K. M. Pohl, M. Bilello, L. Cirillo, G. Biros et al., Glistr: glioma image segmentation and registration, IEEE transactions on medical imaging, vol.31, pp.1941-1954, 2012.

D. Kwon, R. T. Shinohara, H. Akbari, and C. Davatzikos, Combining generative models for multifocal glioma segmentation and registration, International Conference on Medical Image Computing and ComputerAssisted Intervention, pp.763-770, 2014.

N. Cordier, H. Delingette, and N. Ayache, A patch-based approach for the segmentation of pathologies: Application to glioma labelling, IEEE transactions on medical imaging, vol.35, pp.1066-1076, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01241480

S. Bauer, L. Nolte, and M. Reyes, Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.354-361, 2011.

C. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander et al., Segmenting brain tumors with conditional random fields and support vector machines, International Workshop on Computer Vision for Biomedical Image Applications, pp.469-478, 2005.

T. K. Ho, Random decision forests, Proceedings of the Third International Conference on, vol.1, pp.278-282, 1995.

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision forests for tissuespecific segmentation of high-grade gliomas in multi-channel mr, International Conference on Medical Image Computing and ComputerAssisted Intervention, pp.369-376, 2012.

E. Geremia, B. H. Menze, and N. Ayache, Spatial decision forests for glioma segmentation in multi-channel mr images, MICCAI Challenge on Multimodal Brain Tumor Segmentation, vol.34, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00813827

L. L. Folgoc, A. V. Nori, S. Ancha, and A. Criminisi, Lifted auto-context forests for brain tumour segmentation, International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp.171-183, 2016.

S. Bauer, T. Fejes, J. Slotboom, R. Wiest, L. Nolte et al., Segmentation of brain tumor images based on integrated hierarchical classification and regularization, MICCAI BraTS Workshop, 2012.

N. J. Tustison, K. Shrinidhi, M. Wintermark, C. R. Durst, B. M. Kandel et al., Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with antsr, Neuroinformatics, vol.13, pp.209-225, 2015.

Y. Lecun and Y. Bengio, Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, vol.3361, 1995.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, pp.1097-1105, 2012.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, 2014.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus et al., Overfeat: Integrated recognition, localization and detection using convolutional networks, 2013.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected crfs, 2014.

S. Pereira, A. Pinto, V. Alves, and C. A. Silva, Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri, in: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp.131-143, 2015.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane et al., Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation, 2016.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234-241, 2015.

M. Havaei, A. Davy, D. Warde-farley, A. Biard, A. Courville et al., Brain tumor segmentation with deep neural networks, Medical image analysis, vol.35, pp.18-31, 2017.

Q. Zheng, H. Delingette, N. Duchateau, and N. , Ayache, 3d consistent & robust segmentation of cardiac images by deep learning with spatial propagation, IEEE Transactions on Medical Imaging, 2018.

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, 2017.

O. C-¸-içek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, Learning dense volumetric segmentation from sparse annotation, 2016.

K. Kamnitsas, W. Bai, E. Ferrante, S. Mcdonagh, M. Sinclair et al., Ensembles of multiple models and architectures for robust brain tumour segmentation, 2017.

G. Wang, W. Li, S. Ourselin, and T. Vercauteren, Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks, 2017.
DOI : 10.1007/978-3-319-75238-9_16

F. Isensee, P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-hein, Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge, International MICCAI BraTS Challenge, 2017.
DOI : 10.1007/978-3-319-75238-9_25

J. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, 2001.

J. Serra and P. Soille, Mathematical morphology and its applications to image processing, vol.2, 2012.

S. E. Dreyfus, Artificial neural networks, back propagation, and the kelley-bryson gradient procedure, Journal of Guidance, Control, and Dynamics, vol.13, pp.926-928, 1990.

F. Ciompi, K. Chung, S. J. Van-riel, A. A. Setio, P. K. Gerke et al., Towards automatic pulmonary nodule management in lung cancer screening with deep learning, Scientific Reports, vol.7, 2017.
DOI : 10.1038/srep46479

URL : https://www.nature.com/articles/srep46479.pdf

J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu et al., Theano: A cpu and gpu math compiler in python, Proc. 9th Python in Science Conf, pp.1-7, 2010.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Cognitive modeling, vol.5, p.1, 1988.
DOI : 10.1038/323533a0

L. G. Nyúl, J. K. Udupa, and X. Zhang, New variants of a method of mri scale standardization, IEEE transactions on medical imaging, vol.19, pp.143-150, 2000.

J. G. Sled, A. P. Zijdenbos, and A. C. Evans, A nonparametric method for automatic correction of intensity nonuniformity in mri data, IEEE transactions on medical imaging, vol.17, pp.87-97, 1998.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.