Automatic multiple sclerosis lesion segmentation with P-LOCUS

Senan Doyle 1 Florence Forbes 2 Michel Dojat 3
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : P-LOCUS provides automatic quantitative neuroimaging bio-marker extraction tools to aid diagnosis, prognosis and follow-up in multiple sclerosis studies. The software performs accurate and precise seg-mentation of multiple sclerosis lesions in a multi-stage process. In the first step, a weighted Gaussian tissue model is used to perform a robust segmentation. The algorithm avails of complementary information from multiple MR sequences, and includes additional estimated weight variables to account for the relative importance of each voxel. These estimated weights are used to define candidate lesion voxels that are not well described by a normal tissue model. In the second step, the candidate le-sion regions are used to populate the weighted Gaussian model and guide convergence to an optimal solution. The segmentation is unsupervised, removing the need for a training dataset, and providing independence from specific scanner type and MRI scanner protocol.
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https://www.hal.inserm.fr/inserm-01417434
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Submitted on : Thursday, December 15, 2016 - 4:06:03 PM
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Senan Doyle, Florence Forbes, Michel Dojat. Automatic multiple sclerosis lesion segmentation with P-LOCUS. Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure — MICCAI-MSSEG, pp.17-21, 2016. ⟨inserm-01417434⟩

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