Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

Détails

Ressource 1Télécharger: 32663798_BIB_5FF43232BB1C.pdf (2323.28 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_5FF43232BB1C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.
Périodique
NeuroImage. Clinical
Auteur⸱e⸱s
La Rosa F., Abdulkadir A., Fartaria M.J., Rahmanzadeh R., Lu P.J., Galbusera R., Barakovic M., Thiran J.P., Granziera C., Bach Cuadra M.
ISSN
2213-1582 (Electronic)
ISSN-L
2213-1582
Statut éditorial
Publié
Date de publication
30/06/2020
Peer-reviewed
Oui
Volume
27
Pages
102335
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Résumé
The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners.
Mots-clé
CNN, Cortical lesions, FLAIR, MP2RAGE, MRI, Multiple sclerosis, Segmentation, U-Net
Pubmed
Open Access
Oui
Création de la notice
24/07/2020 13:02
Dernière modification de la notice
07/05/2021 6:35
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