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.
Keywords : MRI Statistics Multiple Sclerosis Intensity Normalization
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Conference papers
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https://www.hal.inserm.fr/inserm-01424802
Contributor : Olivier Commowick <>
Submitted on : Monday, January 2, 2017 - 9:59:02 PM
Last modification on : Monday, August 5, 2019 - 5:56:07 PM
Long-term archiving on: Tuesday, April 4, 2017 - 1:41:51 AM

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  • HAL Id : inserm-01424802, version 1

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CNRS | INSTITUT-TELECOM | UNIV-RENNES1 | INRIA | IRISA | UR1-UFR-ISTIC | UNIV-RENNES | IRISA-D5

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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⟩

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