Automatic multiple sclerosis lesion segmentation with P-LOCUS - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2016

Automatic multiple sclerosis lesion segmentation with P-LOCUS

Résumé

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.
Fichier principal
Vignette du fichier
Doyle et al, MSSEG Challenge Proceedings.pdf (803.62 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inserm-01417434 , version 1 (15-12-2016)

Identifiants

  • HAL Id : inserm-01417434 , version 1

Citer

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⟩
404 Consultations
136 Téléchargements

Partager

Gmail Facebook X LinkedIn More