Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

Jeremy Beaumont; Olivier Commowick; Christian Barillot

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

Document type :

Conference papers

Domain :

Life Sciences [q-bio] / Neurons and Cognition [q-bio.NC]

Life Sciences [q-bio] / Bioengineering

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https://www.hal.inserm.fr/inserm-01417378
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Submitted on : Thursday, December 15, 2016 - 3:41:46 PM
Last modification on : Monday, March 4, 2019 - 2:07:47 PM
Long-term archiving on : Thursday, March 16, 2017 - 6:02:15 PM

Beaumont et al, MSSEG_Challeng...

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Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

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