Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Medical image analysis, vol.17, pp.1-18, 2013. ,
Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut, 2016. ,
URL : https://hal.archives-ouvertes.fr/inserm-01417378
An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis, Neuroimage, vol.59, issue.4, pp.3774-3783, 2012. ,
Random Forest for Multiple Sclerosis Lesion Segmentation, MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, p.81, 2016. ,
Rotation-invariant multi-contrast non-local means for MS lesion segmentation, Neuroimage Clin, vol.8, pp.376-89, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01178544
Patch-based segmentation with spatial consistency: application to MS lesions in brain MRI, Journal of Biomedical Imaging, pp.1687-4188, 2016. ,
Tversky as a Loss Function for Highly Unbalanced Image Segmentation using 3D Fully Convolutional Deep Networks, 2018. ,
Nabla-net: a deep dag-like convolutional architecture for biomedical image segmentation: application to white-matter lesion segmentation in multiple sclerosis, MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, p.37, 2016. ,
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach, NeuroImage, vol.155, pp.159-168, 2017. ,
, MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01397806
Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria, The Lancet Neurology, pp.1474-4422, 2017. ,
Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis. Hum Brain Mapp, vol.38, pp.5501-5518, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01563131
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability, Neuroimage, vol.31, issue.3, pp.1116-1144, 2006. ,
BEaST: brain extraction based on nonlocal segmentation technique, Neuroimage, vol.59, issue.3, pp.2362-73, 2012. ,
URL : https://hal.archives-ouvertes.fr/inserm-00629187
Adaptive non-local means denoising of MR images with spatially varying noise levels, J Magn Reson Imaging, vol.31, issue.1, pp.192-203, 2010. ,
URL : https://hal.archives-ouvertes.fr/inserm-00454564
N4ITK: improved N3 bias correction, IEEE Trans Med Imaging, vol.29, issue.6, pp.1310-1330, 2010. ,
A reproducible evaluation of ANTs similarity metric performance in brain image registration, Neuroimage, vol.54, issue.3, pp.2033-2044, 2011. ,
Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT), Neuroimage, vol.54, issue.3, pp.2116-2140, 2011. ,
Improved estimates of partial volume coefficients from noisy brain MRI using spatial context, Neuroimage, vol.53, issue.2, pp.480-490, 2010. ,
volBrain: An Online MRI Brain Volumetry System. Front Neuroinform, vol.10, p.30, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01180069
Nonlocal intracranial cavity extraction, Int J Biomed Imaging, p.820205, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01060348
NABS: non-local automatic brain hemisphere segmentation, Magn Reson Imaging, vol.33, issue.4, pp.474-84, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01116696
Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation, Neuroimage, vol.54, issue.2, pp.940-54, 2011. ,
An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images, IEEE transactions on medical imaging, vol.27, issue.4, pp.425-441, 2008. ,
URL : https://hal.archives-ouvertes.fr/inserm-00169658
A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation, Neuroimage, vol.55, issue.3, pp.968-85, 2011. ,
HIPS: A new hippocampus subfield segmentation method, NeuroImage, vol.163, pp.286-295, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01643644