ArticleFrontiers in neuroscience2024
Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Typical development of the human fetal subplate: Regional heterogeneity, growth, and asymmetry assessed by in vivo T2-weighted MRI.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.Frontiers in neuroscience · 2026Article
- Typical development of the human fetal subplate: regional heterogeneity, growth, and asymmetry assessed by in vivo T2-weighted MRI.bioRxiv : the preprint server for biology · 2025Article
- Conditional deep generative normative modeling for structural and developmental anomaly detection in the fetal brain.NeuroImage · 2025Article
- Application of improved graph convolutional network for cortical surface parcellation.Scientific reports · 2025Article
- Foetal cortical expansion is associated with neurodevelopmental outcome at 2-years in congenital heart disease: a longitudinal follow-up study.EBioMedicine · 2025Article
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7 authors.
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Abstract
Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.
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