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Related Experiment Video

Updated: Jul 13, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

DEEP-GYRALNET: ENABLING MACHINE LEARNING IN GYRAL FOLDING PATTERN EXTRACTION ON CORTICAL SURFACE.

Chao Cao1, Jiale Cheng2, Minheng Chen1

  • 1Department of Computer Science and Engineering, University of Texas at Arlington, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 12, 2026
PubMed
Summary

We developed Deep-GyralNet, a deep learning method for efficiently identifying brain's 3-hinge gyri (3HG) and GyralNet structures. This approach significantly speeds up analysis for large-scale brain studies.

Keywords:
3HGCortical Folding PatternGyralNetSpherical U-Net

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Last Updated: Jul 13, 2026

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09:57

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Published on: January 2, 2012

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Published on: June 30, 2018

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • The 3-hinge gyrus (3HG) is a critical brain hub connecting gyri.
  • Extracting the GyralNet, formed by these hubs, is complex and hinders scalability.

Purpose of the Study:

  • To introduce Deep-GyralNet, a deep learning framework for efficient 3HG and GyralNet extraction.
  • To overcome the limitations of traditional geometric methods in analyzing brain structures.

Main Methods:

  • Utilized a Spherical U-Net for gyral crest representation learning from cortical morphology.
  • Implemented a Connectivity-Aware Path Enforcement loss for topological continuity.
  • Applied minimum spanning tree refinement for structural connectivity and valid GyralNet output.

Main Results:

  • Achieved 98% average completeness in 3HG and GyralNet extraction.
  • Reduced processing time by 94% compared to conventional pipelines.
  • Demonstrated high accuracy and efficiency on the Human Connectome Project dataset.

Conclusions:

  • Deep-GyralNet is the first deep learning solution for rapid and accurate 3HG identification.
  • Enables large-scale investigations into gyral folding networks across brain development, aging, and disorders.