Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.
Journal
IEEE transactions on medical imaging
ISSN: 1558-254X
Titre abrégé: IEEE Trans Med Imaging
Pays: United States
ID NLM: 8310780
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
aheadofprint
Résumé
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average 95
Identifiants
pubmed: 39475746
doi: 10.1109/TMI.2024.3485554
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM