, 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.
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. ,
,
Classifier selection strategies for label fusion using large atlas databases, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.523-531, 2007. ,
Multi-atlas based segmentation of brain images : atlas selection and its effect on accuracy, Neuroimage, vol.46, issue.3, pp.726-738, 2009. ,
Automatic segmentation of eight tissue classes in neonatal brain mri, PLOS ONE, vol.8, issue.12, 2013. ,
Probabilistic segmentation of brain tissue in mr imaging, NeuroImage, vol.27, issue.4, pp.795-804, 2005. ,
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. ,
A fast diffeomorphic image registration algorithm, Neuroimage, vol.38, issue.1, pp.95-113, 2007. ,
Non-local statistical label fusion for multi-atlas segmentation, Medical image analysis, vol.17, issue.2, pp.194-208, 2013. ,
, , 2017.
, Segnet : A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence
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. ,
, PHiSeg : Capturing Uncertainty in Medical Image Segmentation. arXiv e-prints, 2019.
Intensity non-uniformity correction in mri : Existing methods and their validation, European conference on computer vision, vol.10, pp.234-246, 2006. ,
Topology aware fully convolutional networks for histology gland segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.460-468, 2016. ,
Large-scale machine learning with stochastic gradient descent, Proceedings of COMPSTAT'2010, pp.177-186, 2010. ,
Deeplab : Semantic image segmentation with deep convolutional nets, atrous convolution, 2016. ,
, Semantic image segmentation with deep convolutional nets and fully connected crfs, 2014.
Deeplab : Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE transactions, vol.40, issue.4, pp.834-848, 2017. ,
Rethinking atrous convolution for semantic image segmentation, 2017. ,
Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema, Biomed. Opt. Express, vol.6, issue.4, pp.1172-1194, 2015. ,
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
,
Mean shift : A robust approach toward feature space analysis, INSA de Lyon, tous droits réservés, pp.603-619, 2002. ,
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. ,
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
Support-vector networks, Machine Learning, vol.20, issue.3, pp.273-297, 1995. ,
Patch-based segmentation using expert priors : Application to hippocampus and ventricle segmentation, NeuroImage, vol.54, issue.2, pp.940-954, 2011. ,
, Deep Neural Networks for Anatomical Brain Segmentation, 2015.
Regional head tissue conductivity estimation for improved eeg analysis, IEEE Transactions on Biomedical Engineering, vol.47, issue.12, pp.1584-1592, 2000. ,
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. ,
On bias, variance, 0/1-loss, and the curse-ofdimensionality, Data mining and knowledge discovery, vol.1, issue.1, pp.55-77, 1997. ,
Spatial registration and normalization of images, Human brain mapping, vol.3, issue.3, pp.165-189, 1995. ,
Development of volume conductor and source models to localize epileptic foci, Journal of Clinical Neurophysiology, vol.24, issue.2, pp.101-119, 2007. ,
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
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
, , 2019.
, Removing segmentation inconsistencies with semi-supervised non-adjacency constraint, Medical Image Analysis, vol.58, p.101551
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. ,
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. ,
Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010. ,
Deep learning, 2016. ,
, , 2013.
, Deep compression : Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015.
Brain tumor segmentation with deep neural networks, Medical Image Analysis, vol.35, pp.18-31, 2017. ,
Delving deep into rectifiers : Surpassing human-level performance on imagenet classification, Proceedings of the IEEE international conference on computer vision, pp.1026-1034, 2015. ,
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. ,
,
Automatic anatomical brain mri segmentation combining label propagation and decision fusion, INSA de Lyon, tous droits réservés, vol.33, pp.115-126, 2006. ,
Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4700-4708, 2017. ,
Automated MRI segmentation for individualized modeling of current flow in the human head, Journal of Neural Engineering, vol.10, issue.6, p.66004, 2013. ,
Improved optimization for the robust and accurate linear registration and motion correction of brain images, Neuroimage, vol.17, issue.2, pp.825-841, 2002. ,
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. ,
Constrained-cnn losses for weakly supervised segmentation, Medical Image Analysis, vol.54, pp.88-99, 2019. ,
Mindboggling morphometry of human brains, PLOS Computational Biology, vol.13, issue.2, pp.1-40, 2017. ,
Efficient inference in fully connected crfs with gaussian edge potentials, Advances in neural information processing systems, pp.109-117, 2011. ,
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Brain atrophy progress detection in mr iimages, Journal of Medical Informatics and Technologies, p.16, 2010. ,
, , 1992.
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. ,
, MICCAI Grand Challenge and Workshop on Multi-Atlas Labeling, 2012.
, 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.
, Hyperband : A novel bandit-based approach to hyperparameter optimization, 2016.
, Network in network, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01551350
Progressive neural architecture search, Proceedings of the European Conference on Computer Vision (ECCV), pp.19-34, 2018. ,
Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015. ,
Distinctive image features from scale-invariant keypoints, International journal of computer vision, vol.60, issue.2, pp.91-110, 2004. ,
Rectifier nonlinearities improve neural network acoustic models ,
New methods of mr image intensity standardization via generalized scale, Medical Physics, vol.33, issue.9, pp.3426-3434, 2006. ,
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. ,
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. ,
,
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
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network, IEEE Transactions on Medical Imaging, vol.35, issue.5, pp.1252-1261, 2016. ,
Ray : A distributed framework for emerging {AI} applications, 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18), pp.561-577, 2018. ,
Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), pp.807-814, 2010. ,
Numerical optimization, 2006. ,
Learning deconvolution network for semantic segmentation, Proceedings of the IEEE international conference on computer vision, pp.1520-1528, 2015. ,
New variants of a method of mri scale standardization, IEEE Transactions on Medical Imaging, vol.19, issue.2, pp.143-150, 2000. ,
Anatomically constrained neural networks (acnns) : Application to cardiac image enhancement and segmentation, IEEE TMI, vol.37, issue.2, pp.384-395, 2018. ,
, Cardiac MRI Segmentation with Strong Anatomical Guarantees. arXiv e-prints, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02318818
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.
Learning and incorporating shape models for semantic segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.203-211, 2017. ,
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. ,
U-net : Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015, pp.234-241, 2015. ,
The perceptron : a probabilistic model for information storage and organization in the brain, Psychological review, vol.65, issue.6, p.386, 1958. ,
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
Error corrective boosting for learning fully convolutional networks with limited data, MICCAI, pp.231-239, 2017. ,
, The basic theory. Backpropagation : Theory, architectures and applications, pp.1-34, 1995.
, Evaluating the Search Phase of Neural Architecture Search. arXiv e-prints, 2019.
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
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
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
,
, Enhancing atlas based segmentation with multiclass linear classifiers, INSA de Lyon, tous droits réservés, vol.42, p.7169, 2015.
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
Best practices for convolutional neural networks applied to visual document analysis, Seventh International Conference on Document Analysis and Recognition, pp.958-963, 2003. ,
A generalized network for {mri} intensity normalization, International Conference on Medical Imaging with Deep Learning -Extended Abstract Track, 2019. ,
A longitudinal study of brain atrophy in relapsing multiple sclerosis, Neurology, vol.53, issue.1, pp.139-139, 1999. ,
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. ,
Practical bayesian optimization of machine learning algorithms, Advances in neural information processing systems, pp.2951-2959, 2012. ,
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
Dropout : a simple way to prevent neural networks from overfitting, Journal of machine learning research, vol.15, issue.1, pp.1929-1958, 2014. ,
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. ,
, , 2017.
, Inception-v4, inception-resnet and the impact of residual connections on learning, Thirty-First AAAI Conference on Artificial Intelligence
Spline-based image registration, International Journal of Computer Vision, vol.22, issue.3, pp.199-218, 1997. ,
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
N4itk : improved n3 bias correction, IEEE TMI, vol.29, issue.6, pp.1310-1320, 2010. ,
Diffeomorphic demons : Efficient non-parametric image registration, vol.45, pp.61-72, 2009. ,
URL : https://hal.archives-ouvertes.fr/inserm-00349600
Multispectral brain tissue segmentation using automatically trained k-nearest-neighbor classification, NeuroImage, vol.37, issue.1, pp.71-81, 2007. ,
Multi-atlas segmentation with joint label fusion, vol.35, pp.611-623, 2012. ,
Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation, Frontiers in neuroinformatics, vol.7, 2013. ,
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. ,
Internet Brain Segmentation Repository, 2003. ,
Optimum template selection for atlas-based segmentation, NeuroImage, vol.34, issue.4, pp.1612-1618, 2007. ,
, Exploring Randomly Wired Neural Networks for Image Recognition. arXiv e-prints, 2019.
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. ,
,
, Multi-scale context aggregation by dilated convolutions, 2015.
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
, Neural architecture search with reinforcement learning, 2016.
,