Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling

Détails

ID Serval
serval:BIB_147DE58925E1
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling
Titre de la conférence
Lecture Notes in Computer Science
Auteur(s)
Fartaria Mário João, Roche Alexis, Meuli Reto, Granziera Cristina, Kober Tobias, Bach Cuadra Meritxell
Editeur
Springer International Publishing
ISBN
9783319661780
9783319661797
ISSN
0302-9743
1611-3349
Statut éditorial
Publié
Date de publication
2017
Peer-reviewed
Oui
Pages
142-149
Langue
anglais
Résumé
We propose a novel method to automatically detect and segment multiple sclerosis lesions, located both in white matter and in the cortex. The algorithm consists of two main steps: (i) a supervised approach that outputs an initial bitmap locating candidates of lesional tissue and (ii) a Bayesian partial volume estimation framework that estimates the lesion concentration in each voxel. By using a “mixel” approach, potential partial volume effects especially affecting small lesions can be modeled, thus yielding improved lesion segmentation. The proposed method is tested on multiple MR image sequences including 3D MP2RAGE, 3D FLAIR, and 3D DIR. Quantitative evaluation is done by comparison with manual segmentations on a cohort of 39 multiple sclerosis early-stage patients.
Création de la notice
12/02/2018 17:16
Dernière modification de la notice
21/08/2019 5:33
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