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Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical

Yuanzhuo Zhu1, Xianjun Li2, Chen Niu2

  • 1Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, 710049, China; Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University, Xi'an, 710049, China.

Medical Image Analysis
|February 15, 2025
PubMed
Summary

This study introduces Cortex-Diffusion, a novel deep learning method for automatic brain cortical parcellation. It achieves high accuracy and efficiency by directly analyzing 3D surface data, overcoming limitations of traditional methods.

Keywords:
Cortical surface parcellationFull-band spectral accelerationGeometric deep learningHigh-frequency informationNeuroimage computing

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Cortical parcellation is crucial for brain analysis, influencing research and diagnosis.
  • Traditional methods are time-consuming and error-prone due to spherical mapping.
  • Existing deep learning methods struggle with spherical mapping and feature quantification.

Purpose of the Study:

  • To develop a fully automatic cortical parcellation method.
  • To overcome limitations of existing geometric learning approaches.
  • To improve accuracy, efficiency, and robustness in brain cortex analysis.

Main Methods:

  • A full-band spectral-accelerated spatial diffusion strategy for stable information propagation.
  • Direct parcellation of original cortical surfaces in individual space.
  • Construction of a compact deep network (Cortex-Diffusion) using raw 3D vertex coordinates.

Main Results:

  • State-of-the-art parcellation accuracy and efficiency achieved.
  • Demonstrated superior robustness to mesh resolutions and discretization patterns.
  • Effective on both infant and adult brain imaging datasets with minimal parameters (0.49 MB).

Conclusions:

  • Cortex-Diffusion offers a stable and adaptive approach for fine-grained geometric representations.
  • The method enables fully automatic and highly accurate cortical parcellation.
  • This approach advances neuroscientific research and clinical diagnosis through improved brain analysis.