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Conference Papers Year : 2016

Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

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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.
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Dates and versions

inserm-01417378 , version 1 (15-12-2016)

Identifiers

  • HAL Id : inserm-01417378 , version 1

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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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