Skip to Main content Skip to Navigation
Conference papers

Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

Jeremy Beaumont 1 Olivier Commowick 1, * Christian Barillot 1 
* Corresponding author
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 this paper, we present an algorithm for Multiple Sclerosis (MS) lesion segmentation. Our method is fully automated and includes three main steps: 1. the computation of a rough total lesion load in order to optimize the parameter set of the following step; 2. the detection of lesions by graph cut initialized with a robust Expectation-Maximization (EM) algorithm; 3. the application of rules to remove false positives and to adjust the contour of the detected lesions. Our algorithm will be tested on the FLI 2016 MSSEG challenge data.
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download
Contributor : Nathalie Duchange Connect in order to contact the contributor
Submitted on : Thursday, December 15, 2016 - 3:41:46 PM
Last modification on : Wednesday, February 2, 2022 - 3:53:30 PM
Long-term archiving on: : Thursday, March 16, 2017 - 6:02:15 PM


Beaumont et al, MSSEG_Challeng...
Files produced by the author(s)


  • HAL Id : inserm-01417378, version 1


Jeremy Beaumont, Olivier Commowick, Christian Barillot. Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut. 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. pp.1-8. ⟨inserm-01417378⟩



Record views


Files downloads