Residual-atrous attention network for lumbosacral plexus segmentation with MR image.
Atrous convolution
Lumbosacral plexus
Multi-scale attention
Residual skip connection
Segmentation
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
ISSN: 1879-0771
Titre abrégé: Comput Med Imaging Graph
Pays: United States
ID NLM: 8806104
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
19
04
2022
revised:
12
07
2022
accepted:
28
07
2022
pubmed:
17
8
2022
medline:
9
9
2022
entrez:
16
8
2022
Statut:
ppublish
Résumé
Accurate segmentation of the lumbosacral plexus is a crucial step for diagnosis and analysis of nerve damage in clinical. Due to the extremely low contrast and complicated structure around the lumbosacral plexus, it has been remaining a challenging task to effectively segment the lumbosacral plexus from spinal MR images. Even though several deep learning methods for spine segmentation have been developed, most of them only pay attention to the segmentation of vertebral bodies and intervertebral discs rather than nerves. To solve these problems, in this paper, we propose a residual-atrous attention network (RA
Identifiants
pubmed: 35973284
pii: S0895-6111(22)00079-9
doi: 10.1016/j.compmedimag.2022.102109
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
102109Informations de copyright
Copyright © 2022 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.