Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.
Convolutional neural networks
MRI
Multiple sclerosis
Segmentation
Spinal cord
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 01 2019
01 01 2019
Historique:
received:
08
05
2018
revised:
05
09
2018
accepted:
28
09
2018
pubmed:
10
10
2018
medline:
5
2
2019
entrez:
10
10
2018
Statut:
ppublish
Résumé
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T
Identifiants
pubmed: 30300751
pii: S1053-8119(18)31957-8
doi: 10.1016/j.neuroimage.2018.09.081
pmc: PMC6759925
mid: NIHMS1049773
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT02117375']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
901-915Subventions
Organisme : NEI NIH HHS
ID : R01 EY023240
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS109114
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS087465
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA NS003119-09
Pays : United States
Informations de copyright
Copyright © 2018 Elsevier Inc. All rights reserved.
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