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Updated: Aug 31, 2025

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ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse.

Alberto Fernández-Pena1,2,3, Daniel Martín de Blas1,2,3, Francisco J Navas-Sánchez2,3

  • 1Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.

Neuroinformatics
|August 25, 2022
PubMed
Summary

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This summary is machine-generated.

Automated Brain Lines Extraction (ABLE) reliably segments human cortical folding patterns by focusing on sulcal fundi and gyral crowns. This method accurately captures brain topology, overcoming challenges in traditional sulcal segmentation.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Anatomy

Background:

  • Accurate neuroanatomical segmentation of the human cortex, particularly distinguishing sulci and gyri, remains a significant challenge in neuroscience.
  • The transition zones between sulci and gyri introduce ambiguity in traditional segmentation methods.
  • Focusing on topological opposites—sulcal fundi and gyral crowns—offers a more robust approach to cortical surface analysis.

Purpose of the Study:

  • To introduce Automated Brain Lines Extraction (ABLE), a novel computational method for reliable segmentation of sulcal fundi and gyral crown lines.
  • To address the limitations of existing methods in accurately delineating cortical folding patterns and handling anastomotic sulci.
  • To provide a reproducible and accurate tool for analyzing human cortical topology.
Keywords:
Cortical surfacesGyral crownsStructural MRISulcal lines

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Main Methods:

  • ABLE utilizes Laplacian surface collapse on standard FreeSurfer outputs to segment the cortex into gyral and sulcal surfaces.
  • Spatially filtered surfaces undergo a Laplacian-collapse algorithm for thinning, followed by endpoint detection.
  • Sulcal fundi and gyral crown lines are generated through surface erosion, preserving connectivity.

Main Results:

  • The ABLE method reliably segments sulcal fundi and gyral crown lines, accurately representing cortical topology.
  • ABLE effectively ignores anastomotic sulci and avoids overestimation of sulcal fundi lines.
  • Validation using the Human Connectome Project (HCP) database demonstrates ABLE's high reproducibility compared to other methods.

Conclusions:

  • Automated Brain Lines Extraction (ABLE) provides a reliable and accurate method for segmenting key features of cortical folding.
  • The approach overcomes limitations of previous methods, offering improved topological representation of the human brain's surface.
  • ABLE is publicly available, facilitating further research in neuroanatomy and computational neuroscience.