ArticleProceedings. IEEE International Symposium on Biomedical Imaging2020
AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Variable Presence of an Evolutionarily New Brain Structure Is Related to Trait Impulsivity.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Emotion-related impulsivity is related to orbitofrontal cortical sulcation.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Variable Presence of an Evolutionarily New Brain Structure is Related to Trait Impulsivity.bioRxiv : the preprint server for biology · 2024Article
- Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net.Frontiers in neuroscience · 2024Article
- Sulcal depth in prefrontal cortex: a novel predictor of working memory performance.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces.Proceedings of machine learning research · 2022Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
- Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.NeuroImage · 2021Article
- Overlooked Tertiary Sulci Serve as a Meso-Scale Link between Microstructural and Functional Properties of Human Lateral Prefrontal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
In this paper, we present the automatic labeling framework for sulci in the human lateral prefrontal cortex (PFC). We adapt an existing spherical U-Net architecture with our recent surface data augmentation technique to improve the sulcal labeling accuracy in a developmental cohort. Specifically, our framework consists of the following key components: (1) augmented geometrical features being generated during cortical surface registration, (2) spherical U-Net architecture to efficiently fit the augmented features, and (3) postrefinement of sulcal labeling by optimizing spatial coherence via a graph cut technique. We validate our method on 30 healthy subjects with manual labeling of sulcal regions within PFC. In the experiments, we demonstrate significantly improved labeling performance (0.7749) in mean Dice overlap compared to that of multi-atlas (0.6410) and standard spherical U-Net (0.7011) approaches, respectively (p < 0.05). Additionally, the proposed method achieves a full set of sulcal labels in 20 seconds in this developmental cohort.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.