Harmonized Segmentation of Neonatal Brain MRI.
cortical thickness
deep learning
neonatal brain
segmentation
unsupervised domain adaptation
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2021
2021
Historique:
received:
31
01
2021
accepted:
21
04
2021
entrez:
14
6
2021
pubmed:
15
6
2021
medline:
15
6
2021
Statut:
epublish
Résumé
Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on
Identifiants
pubmed: 34121991
doi: 10.3389/fnins.2021.662005
pmc: PMC8195278
doi:
Types de publication
Journal Article
Langues
eng
Pagination
662005Subventions
Organisme : Medical Research Council
ID : MR/K006355/1
Pays : United Kingdom
Informations de copyright
Copyright © 2021 Grigorescu, Vanes, Uus, Batalle, Cordero-Grande, Nosarti, Edwards, Hajnal, Modat and Deprez.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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