Titre
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
Type
article de conférence/colloque
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
La Rosa, Francesco
Auteure/Auteur
Beck, Erin S.
Auteure/Auteur
Abdulkadir, Ahmed
Auteure/Auteur
Thiran, Jean-Philippe
Auteure/Auteur
Reich, Daniel S.
Auteure/Auteur
Sati, Pascal
Auteure/Auteur
Bach Cuadra, Meritxell
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Maison d’édition
Prof. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Prof. Leo Joskowicz
Titre du livre ou conférence/colloque
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Unité
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
Statut éditorial
Publié
Date de publication
2020-10-04
Peer-reviewed
Oui
Langue
anglais
Résumé
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra- high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 μL, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation.
Sujets
PID Serval
serval:BIB_6CDC75E672E9
Date de création
2021-02-05T15:43:35.483Z
Date de création dans IRIS
2025-05-21T03:34:42Z
Fichier(s)![Vignette d'image]()
En cours de chargement...
Nom
2008.06780.pdf
Version du manuscrit
preprint
Taille
1.94 MB
Format
Adobe PDF
PID Serval
serval:BIB_6CDC75E672E9.P001
URN
urn:nbn:ch:serval-BIB_6CDC75E672E97
Somme de contrôle
(MD5):4df3d0f8c4ab923a589eb57423143fd9