Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

Détails

Ressource 1Télécharger: 2008.06780.pdf (1985.21 [Ko])
Etat: Public
Version: de l'auteur
Licence: Non spécifiée
ID Serval
serval:BIB_6CDC75E672E9
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
Institution
Titre
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
Titre de la conférence
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Auteur(s)
La Rosa Francesco, Beck Erin S., Abdulkadir Ahmed, Thiran Jean-Philippe, Reich Daniel S., Sati Pascal, Bach Cuadra Meritxell
Editeur
Prof. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Prof. Leo Joskowicz
Organisation
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
Statut éditorial
Publié
Date de publication
04/10/2020
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.
Mots-clé
MRI, Ultra-high field, Multiple Sclerosis, Cortical lesions, Segmentation, CNN
Création de la notice
05/02/2021 17:43
Dernière modification de la notice
06/02/2021 8:09
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