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Related Experiment Video

Updated: Jul 6, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Automatic segmentation of human brain sulci.

Faguo Yang1, Frithjof Kruggel

  • 1Signal and Image Processing Laboratory, Department of Biomedical Engineering, University of California, Irvine, CA 92697-2755, United States.

Medical Image Analysis
|March 8, 2008
PubMed
Summary

This study presents an automated algorithm for accurately segmenting neocortical sulci, crucial for brain mapping. The method reliably identifies and labels these complex brain structures, aiding anatomical and functional studies.

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Area of Science:

  • Neuroscience
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • The neocortical surface features intricate folds (gyri) and fissures (sulci).
  • Sulci serve as vital macroscopic landmarks for cortical orientation.
  • Accurate sulcal segmentation and labeling are essential for human brain mapping studies linking brain structure and function.

Purpose of the Study:

  • To develop an automatic algorithm for precise neocortical sulcal segmentation.
  • To address the challenges posed by the structural complexity and inter-subject variability of sulci.

Main Methods:

  • A Bayesian framework is employed to classify white/gray matter interface mesh vertices.
  • Classification utilizes geodesic depth and local curvature information to distinguish gyral and sulcal compartments.

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

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Last Updated: Jul 6, 2026

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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  • A watershed-like growing method is used to aggregate vertices into distinct sulcal regions.

    • Main Results:

    • The proposed algorithm accurately segments neocortical sulci.
    • The method demonstrates robustness in handling variations in sulcal structures.
    • Experimental results validate the precision and reliability of the automated segmentation.

    Conclusions:

    • The developed automatic algorithm effectively segments neocortical sulci.
    • This method offers a robust solution for sulcal analysis in brain mapping.
    • Accurate sulcal segmentation facilitates a deeper understanding of brain anatomy and function.