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Communication Dans Un Congrès Année : 2016

Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

Résumé

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 et versions

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

Identifiants

  • HAL Id : inserm-01417378 , version 1

Citer

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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