ArticleJournal of neuroscience methods2019
Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.
Article in Journal of neuroscience methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.IEEE transactions on medical imaging · 2025Article
- Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.NeuroImage · 2021Article
- Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia.Translational psychiatry · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundHuman cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability. NEW
methodWe propose a fully automated pipeline that integrates both sulcal curve extraction and labeling. In this study, we use a large normal control population (n = 1424) to train neural networks for accurately labeling the primary sulci. Briefly, we use sulcal curve distance map, surface parcellation, mean curvature and spectral features to delineate their sulcal labels. We evaluate the proposed method with 8 primary sulcal curves in the left and right hemispheres compared to an established multi-atlas curve labeling method.
resultsSulcal labels by the proposed method reasonably well agree with manual labeling. The proposed method outperforms the existing multi-atlas curve labeling method. COMPARISON WITH EXISTING
methodSignificantly improved sulcal labeling results are achieved with over 12.5 and 20.6 percent improvement on labeling accuracy in the left and right hemispheres, respectively compared to that of a multi-atlas curve labeling method in eight curves (p≪0.001, two-sample t-test).
conclusionThe proposed method offers a computationally efficient and robust labeling of major sulci.
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