, de l'apprentissage est un autre avantage majeur, car elle donne la possibilité d'entraîner des modèles sur de plus grands ensembles de données

, Ce travail à donné lieu à une présentation orale lors de la conférence, 2018.

[. Ganaye, Une version étendue des travaux a été acceptée pour publication dans la revue spéciale MICCAI 2018 du journal Medical Image Analysis (MIA) en Octobre, 2018.

P. Ganaye,

A. , Classifier selection strategies for label fusion using large atlas databases, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.523-531, 2007.

A. , Multi-atlas based segmentation of brain images : atlas selection and its effect on accuracy, Neuroimage, vol.46, issue.3, pp.726-738, 2009.

. Anbeek, Automatic segmentation of eight tissue classes in neonatal brain mri, PLOS ONE, vol.8, issue.12, 2013.

. Anbeek, Probabilistic segmentation of brain tissue in mr imaging, NeuroImage, vol.27, issue.4, pp.795-804, 2005.

. Artaechevarria, Combination strategies in multi-atlas image segmentation : application to brain mr data, IEEE transactions on medical imaging, vol.28, issue.8, pp.1266-1277, 2009.

J. Ashburner, A fast diffeomorphic image registration algorithm, Neuroimage, vol.38, issue.1, pp.95-113, 2007.

A. J. Asman-and-landman-;-asman and B. A. Landman, Non-local statistical label fusion for multi-atlas segmentation, Medical image analysis, vol.17, issue.2, pp.194-208, 2013.

[. Badrinarayanan, , 2017.

, Segnet : A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence

[. Bai, A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement : application to cardiac mr images, IEEE transactions on medical imaging, vol.32, issue.7, pp.1302-1315, 2013.

[. Baumgartner, PHiSeg : Capturing Uncertainty in Medical Image Segmentation. arXiv e-prints, 2019.

[. Bay, Intensity non-uniformity correction in mri : Existing methods and their validation, European conference on computer vision, vol.10, pp.234-246, 2006.

H. Bentaieb, A. Bentaieb, and G. Hamarneh, Topology aware fully convolutional networks for histology gland segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.460-468, 2016.

L. Bottou, Large-scale machine learning with stochastic gradient descent, Proceedings of COMPSTAT'2010, pp.177-186, 2010.

[. Chen, Deeplab : Semantic image segmentation with deep convolutional nets, atrous convolution, 2016.

[. Chen, Semantic image segmentation with deep convolutional nets and fully connected crfs, 2014.

[. Chen, Deeplab : Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE transactions, vol.40, issue.4, pp.834-848, 2017.

[. Chen, Rethinking atrous convolution for semantic image segmentation, 2017.

[. Chiu, Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema, Biomed. Opt. Express, vol.6, issue.4, pp.1172-1194, 2015.

[. Collewet, Influence of mri acquisition protocols and image intensity normalization methods on texture classification, Magnetic Resonance Imaging, vol.22, issue.1, pp.81-91, 2004.
URL : https://hal.archives-ouvertes.fr/hal-02583287

P. Ganaye,

D. Comaniciu and P. Meer, Mean shift : A robust approach toward feature space analysis, INSA de Lyon, tous droits réservés, pp.603-619, 2002.

M. J. Cook and Z. J. Koles, A high-resolution anisotropic finite-volume head model for eeg source analysis, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.4536-4539, 2006.

[. Cordier, A patch-based approach for the segmentation of pathologies : Application to glioma labelling, IEEE Transactions on Medical Imaging, vol.35, issue.4, pp.1066-1076, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01241480

V. Cortes, C. Cortes, and V. Vapnik, Support-vector networks, Machine Learning, vol.20, issue.3, pp.273-297, 1995.

[. Coupé, Patch-based segmentation using expert priors : Application to hippocampus and ventricle segmentation, NeuroImage, vol.54, issue.2, pp.940-954, 2011.

M. Brebisson, A. De-brebisson, and G. Montana, Deep Neural Networks for Anatomical Brain Segmentation, 2015.

[. Ferree, Regional head tissue conductivity estimation for improved eeg analysis, IEEE Transactions on Biomedical Engineering, vol.47, issue.12, pp.1584-1592, 2000.

[. Fisher, Gray matter atrophy in multiple sclerosis : a longitudinal study, Annals of Neurology : Official Journal of the American Neurological Association and the Child Neurology Society, vol.64, issue.3, pp.255-265, 2008.

J. H. Friedman, On bias, variance, 0/1-loss, and the curse-ofdimensionality, Data mining and knowledge discovery, vol.1, issue.1, pp.55-77, 1997.

[. Friston, Spatial registration and normalization of images, Human brain mapping, vol.3, issue.3, pp.165-189, 1995.

. Fuchs, Development of volume conductor and source models to localize epileptic foci, Journal of Clinical Neurophysiology, vol.24, issue.2, pp.101-119, 2007.

[. Ganaye, Semisupervised learning for segmentation under semantic constraint, Medical Image Computing and Computer Assisted Intervention -MICCAI 2018, pp.595-602, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01904641

P. Ganaye and . Ganaye, Towards integrating spatial localization in convolutional neural networks for brain image segmentation, IEEE 15th International Symposium on, pp.621-625, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01812045

[. Ganaye, , 2019.

, Removing segmentation inconsistencies with semi-supervised non-adjacency constraint, Medical Image Analysis, vol.58, p.101551

[. Ghafoorian, Deep multi-scale location-aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin, NeuroImage : Clinical, vol.14, pp.391-399, 2017.

[. Ghafoorian, Deep multi-scale location-aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin, NeuroImage : Clinical, vol.14, pp.391-399, 2017.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010.

[. Goodfellow, Deep learning, 2016.

[. Goodfellow, , 2013.

[. Han, Deep compression : Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015.

[. Havaei, Brain tumor segmentation with deep neural networks, Medical Image Analysis, vol.35, pp.18-31, 2017.

[. He, Delving deep into rectifiers : Surpassing human-level performance on imagenet classification, Proceedings of the IEEE international conference on computer vision, pp.1026-1034, 2015.

[. He, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

P. Ganaye,

. Heckemann, Automatic anatomical brain mri segmentation combining label propagation and decision fusion, INSA de Lyon, tous droits réservés, vol.33, pp.115-126, 2006.

[. Huang, Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4700-4708, 2017.

[. Huang, Automated MRI segmentation for individualized modeling of current flow in the human head, Journal of Neural Engineering, vol.10, issue.6, p.66004, 2013.

[. Jenkinson, Improved optimization for the robust and accurate linear registration and motion correction of brain images, Neuroimage, vol.17, issue.2, pp.825-841, 2002.

. Del-toro, Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms : Visceral anatomy benchmarks, IEEE Transactions on Medical Imaging, vol.35, issue.11, pp.2459-2475, 2016.

[. Kervadec, Constrained-cnn losses for weakly supervised segmentation, Medical Image Analysis, vol.54, pp.88-99, 2019.

[. Klein, Mindboggling morphometry of human brains, PLOS Computational Biology, vol.13, issue.2, pp.1-40, 2017.

K. Krähenbühl, P. Krähenbühl, and V. Koltun, Efficient inference in fully connected crfs with gaussian edge potentials, Advances in neural information processing systems, pp.109-117, 2011.

[. Krizhevsky, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

[. Kuczy?ski, Brain atrophy progress detection in mr iimages, Journal of Medical Informatics and Technologies, p.16, 2010.

[. Kwong, , 1992.

P. Ganaye, Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation, Proceedings of the National Academy of Sciences, vol.89, pp.5675-5679, 1992.

B. Landman, MICCAI Grand Challenge and Workshop on Multi-Atlas Labeling, 2012.

. Lecun, Y. Bengio-;-lecun, and Y. Bengio, The handbook of brain theory and neural networks. chapter Convolutional Networks for Images, Speech, and Time Series, pp.255-258, 1998.

, Towards a deep learning approach to brain parcellation, 2011 IEEE International Symposium on Biomedical Imaging : From Nano to Macro, pp.321-324, 2011.

[. Li, Hyperband : A novel bandit-based approach to hyperparameter optimization, 2016.

. Lin, Network in network, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01551350

[. Liu, Progressive neural architecture search, Proceedings of the European Conference on Computer Vision (ECCV), pp.19-34, 2018.

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

D. G. Lowe, Distinctive image features from scale-invariant keypoints, International journal of computer vision, vol.60, issue.2, pp.91-110, 2004.

[. Maas, Rectifier nonlinearities improve neural network acoustic models

[. Madabhushi, A. Udupa-;-madabhushi, and J. K. Udupa, New methods of mr image intensity standardization via generalized scale, Medical Physics, vol.33, issue.9, pp.3426-3434, 2006.

[. Marcus, Open access series of imaging studies : Longitudinal mri data in nondemented and demented older adults, Journal of Cognitive Neuroscience, vol.22, issue.12, pp.2677-2684, 2010.

[. Marcus, Open access series of imaging studies : longitudinal mri data in nondemented and demented older adults, Journal of cognitive neuroscience, vol.22, issue.12, pp.2677-2684, 2010.

P. Ganaye,

. Milletari, V-net : Fully convolutional neural networks for volumetric medical image segmentation, INSA de Lyon, tous droits réservés, 2016.

, Fourth International Conference on 3D Vision (3DV), pp.565-571

. Moeskops, Automatic Segmentation of MR Brain Images With a Convolutional Neural Network, IEEE Transactions on Medical Imaging, vol.35, issue.5, pp.1252-1261, 2016.

. Moritz, Ray : A distributed framework for emerging {AI} applications, 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18), pp.561-577, 2018.

H. Nair, 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.

W. ;. Nocedal, J. Nocedal, and S. Wright, Numerical optimization, 2006.

[. Noh, Learning deconvolution network for semantic segmentation, Proceedings of the IEEE international conference on computer vision, pp.1520-1528, 2015.

. Nyul, New variants of a method of mri scale standardization, IEEE Transactions on Medical Imaging, vol.19, issue.2, pp.143-150, 2000.

[. Oktay, Anatomically constrained neural networks (acnns) : Application to cardiac image enhancement and segmentation, IEEE TMI, vol.37, issue.2, pp.384-395, 2018.

[. Painchaud, Cardiac MRI Segmentation with Strong Anatomical Guarantees. arXiv e-prints, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02318818

. Phan, Processing of structural neuroimaging data in young children : Bridging the gap between current practice and state-of-the-art methods, Developmental Cognitive Neuroscience, p.33, 2017.

, Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures, NeuroImage, vol.39, issue.1, pp.238-247, 2008.

P. Ganaye-[ravishankar, Learning and incorporating shape models for semantic segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.203-211, 2017.

[. Rohlfing, Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains, NeuroImage, vol.21, issue.4, pp.1428-1442, 2004.

[. Ronneberger, U-net : Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015, pp.234-241, 2015.

F. Rosenblatt, The perceptron : a probabilistic model for information storage and organization in the brain, Psychological review, vol.65, issue.6, p.386, 1958.

[. Rousseau, A supervised patch-based approach for human brain labeling, IEEE transactions on medical imaging, vol.30, issue.10, pp.1852-1862, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00631458

[. Roy, Error corrective boosting for learning fully convolutional networks with limited data, MICCAI, pp.231-239, 2017.

[. Rumelhart, The basic theory. Backpropagation : Theory, architectures and applications, pp.1-34, 1995.

[. Sciuto, Evaluating the Search Phase of Neural Architecture Search. arXiv e-prints, 2019.

M. Sdika, A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization, IEEE Transactions on Medical Imaging, vol.27, pp.271-81, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02073305

M. Sdika, Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote, Linking) Journal Article Research Support, vol.14, issue.2, pp.1361-8415, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00617791

M. Sdika, A Sharp Sufficient Condition for B-Spline Vector Field Invertibility. Application to Diffeomorphic Registration and Interslice Interpolation, SIAM Journal on Imaging Sciences, vol.6, issue.4, pp.2236-2257, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01902498

P. Ganaye,

, Enhancing atlas based segmentation with multiclass linear classifiers, INSA de Lyon, tous droits réservés, vol.42, p.7169, 2015.

. Sdika, M. Pelletier-;-sdika, and D. Pelletier, Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping, Human Brain Mapping, vol.30, pp.1060-1067, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01902507

[. Simard, Best practices for convolutional neural networks applied to visual document analysis, Seventh International Conference on Document Analysis and Recognition, pp.958-963, 2003.

[. Simkó, A generalized network for {mri} intensity normalization, International Conference on Medical Imaging with Deep Learning -Extended Abstract Track, 2019.

. Simon, A longitudinal study of brain atrophy in relapsing multiple sclerosis, Neurology, vol.53, issue.1, pp.139-139, 1999.

. 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.

[. Snoek, Practical bayesian optimization of machine learning algorithms, Advances in neural information processing systems, pp.2951-2959, 2012.

. Sotiras, Deformable medical image registration : A survey, IEEE Transactions on Medical Imaging, vol.32, issue.7, pp.1153-1190, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00684715

[. Srivastava, Dropout : a simple way to prevent neural networks from overfitting, Journal of machine learning research, vol.15, issue.1, pp.1929-1958, 2014.

[. Sudre, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp.240-248, 2017.

[. Szegedy, , 2017.

, Inception-v4, inception-resnet and the impact of residual connections on learning, Thirty-First AAAI Conference on Artificial Intelligence

P. Ganaye, [. Szeliski, R. Coughlan-;-szeliski, and J. Coughlan, Spline-based image registration, International Journal of Computer Vision, vol.22, issue.3, pp.199-218, 1997.

[. Tanenbaum, Synthetic mri for clinical neuroimaging : Results of the magnetic resonance image compilation (magic) prospective, multicenter, 2017.

, American Journal of Neuroradiology, vol.38, issue.6, pp.1103-1110

[. Tustison, N4itk : improved n3 bias correction, IEEE TMI, vol.29, issue.6, pp.1310-1320, 2010.

[. Vercauteren, Diffeomorphic demons : Efficient non-parametric image registration, vol.45, pp.61-72, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00349600

[. Vrooman, Multispectral brain tissue segmentation using automatically trained k-nearest-neighbor classification, NeuroImage, vol.37, issue.1, pp.71-81, 2007.

[. Wang, Multi-atlas segmentation with joint label fusion, vol.35, pp.611-623, 2012.

Y. Wang, H. Wang, and P. A. Yushkevich, Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation, Frontiers in neuroinformatics, vol.7, 2013.

[. Wolters, Influence of tissue conductivity anisotropy on eeg/meg field and return current computation in a realistic head model : A simulation and visualization study using high-resolution finite element modeling, NeuroImage, vol.30, issue.3, pp.813-826, 2006.

A. Worth, Internet Brain Segmentation Repository, 2003.

[. Wu, Optimum template selection for atlas-based segmentation, NeuroImage, vol.34, issue.4, pp.1612-1618, 2007.

[. Xie, Exploring Randomly Wired Neural Networks for Image Recognition. arXiv e-prints, 2019.

[. Yang, Designing energy-efficient convolutional neural networks using energy-aware pruning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5687-5695, 2017.

P. Ganaye,

F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, 2015.

. Yu-feng, Altered baseline brain activity in children with adhd revealed by resting-state functional mri, Brain and Development, vol.29, issue.2, pp.83-91, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00123309

L. ;. Zoph, B. Zoph, and Q. V. Le, Neural architecture search with reinforcement learning, 2016.

P. Ganaye,