Skip to Main content Skip to Navigation
Conference papers

Automatic Multiple Sclerosis lesion segmentation from Intensity-Normalized multi-channel MRI

Jeremy Beaumont 1 Olivier Commowick 1 Christian Barillot 1 
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In the context of the FLI MICCAI 2016 MSSEG challenge for lesion segmentation, we present a fully automated algorithm for Multiple Sclerosis (MS) lesion segmentation. Our method is composed of three main steps. First, the MS patient images are registered and intensity normalized. Then, the lesion segmentation is done using a voxel-wise comparison of multi-channel Magnetic Resonance Images (MRI) against a set of controls. Finally, the segmentation is refined by applying several lesion appearance rules.
Complete list of metadata
Contributor : Olivier Commowick Connect in order to contact the contributor
Submitted on : Monday, January 2, 2017 - 9:59:02 PM
Last modification on : Saturday, June 25, 2022 - 7:46:49 PM
Long-term archiving on: : Tuesday, April 4, 2017 - 1:41:51 AM


Files produced by the author(s)


  • HAL Id : inserm-01424802, version 1


Jeremy Beaumont, Olivier Commowick, Christian Barillot. Automatic Multiple Sclerosis lesion segmentation from Intensity-Normalized multi-channel MRI. Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure - MICCAI-MSSEG, Oct 2016, Athens, Greece. ⟨inserm-01424802⟩



Record views


Files downloads