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.
Keywords : Graph Cut Multiple sclerosis Expectation-Maximization Tissue classification
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Conference papers
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Submitted on : Thursday, December 15, 2016 - 3:41:46 PM
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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⟩

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