A. Bjoern-h-menze, S. Jakab, J. Bauer, K. Kalpathy-cramer, J. Farahani et al., The multimodal brain tumor image segmentation benchmark (brats), IEEE transactions on medical imaging, vol.34, issue.10, pp.1993-2024, 2015.

M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical image analysis, vol.8, issue.3, pp.275-283, 2004.

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision forests for tissue-specific segmentation of 23
DOI : 10.1007/978-3-642-33454-2_46

URL : https://link.springer.com/content/pdf/10.1007%2F978-3-642-33454-2_46.pdf

J. E. Cates, A. E. Lefohn, and R. Whitaker, Gist: an interactive, gpu-based level set segmentation tool for 3d medical images, Medical image analysis, vol.8, issue.3, pp.217-231, 2004.

J. E. Aaron-e-lefohn, R. Cates, and . Whitaker, Interactive, gpu-based level sets for 3d segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.564-572, 2003.

K. Bjoern-h-menze, D. Van-leemput, M. Lashkari, N. Weber, P. Ayache et al., A generative model for brain tumor segmentation in multi-modal images, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.151-159, 2010.

N. Moon, E. Bullitt, K. Van-leemput, and G. Gerig, Model-based brain and tumor segmentation, Proceedings. 16th International Conference on, vol.1, pp.528-531, 2002.

M. Prastawa, E. Bullitt, N. Moon, K. Van-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.

E. Geremia, O. Clatz, H. Bjoern, E. Menze, A. Konukoglu et al., Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images, NeuroImage, vol.57, issue.2, pp.378-390, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00616194

M. Ozkan, M. Benoit, R. Dawant, and . Maciunas, Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study, IEEE transactions on Medical Imaging, vol.12, issue.3, pp.534-544, 1993.

C. Matthew, L. O. Clark, . Hall, B. Dmitry, R. Goldgof et al.,

M. S. Silbiger, Automatic tumor segmentation using knowledge-based techniques, IEEE transactions on medical imaging, vol.17, issue.2, pp.187-201, 1998.

. Lynn-m-fletcher-heath, O. Lawrence, . Hall, B. Dmitry, F. Goldgof et al., Automatic segmentation of non-enhancing brain tumors in magnetic resonance images, Artificial intelligence in medicine, vol.21, issue.1-3, pp.43-63, 2001.

D. Zikic, Y. Ioannou, M. Brown, and A. Criminisi, Segmentation of brain tumor tissues with convolutional neural networks, Proceedings MICCAI-BRATS, pp.36-39, 2014.

G. Urban, . Bendszus, J. Hamprecht, and . Kleesiek, Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winning contribution, pp.31-35, 2014.

D. Axel, H. Mohammad, W. David, B. Antoine, T. Lam et al., Pal Chris, and Bengio Yoshua. Brain tumor segmentation with deep neural networks, Proceedings MICCAI-BRATS, pp.1-05, 2014.

S. Pereira, A. Pinto, V. Alves, and C. Silva, Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri, Proceedings MICCAI-BRATS, pp.52-55, 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.

G. E. David-e-rumelhart, R. Hinton, and . Williams, Learning representations by backpropagating errors, nature, vol.323, issue.6088, p.533, 1986.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), pp.807-814, 2010.

N. Geoffrey-e-hinton, A. Srivastava, I. Krizhevsky, . Sutskever, and . Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, 2012.

B. Zoph, V. Vasudevan, J. Shlens, and Q. Le, Learning transferable architectures for scalable image recognition, vol.2, 2017.

H. Pham, Y. Melody, B. Guan, . Zoph, V. Quoc et al., Efficient neural architecture search via parameter sharing, 2018.