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Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Fenqiang Zhao1, Zhengwang Wu1, Li Wang1

  • 1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA.

Medical Image Analysis
|June 1, 2024
PubMed
Summary

This study introduces a novel semi-supervised learning framework for accurate and consistent longitudinal brain surface registration and parcellation. The method improves tracking of brain changes over time, especially in challenging regions.

Keywords:
Cortical surface registrationLongitudinal analysisParcellation, Spherical network

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Longitudinal analysis of brain surfaces is crucial for understanding brain changes.
  • Existing methods often lack temporal consistency and accuracy in registration and parcellation.
  • Inconsistency is particularly problematic in small or ambiguous cortical regions.

Purpose of the Study:

  • To develop a robust framework for temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces.
  • To leverage the inherent relationships between registration and parcellation tasks.
  • To improve the analysis of longitudinal morphological and functional brain changes.

Main Methods:

  • A novel semi-supervised learning framework utilizing a spherical network encoder.
  • Specialized decoders for registration and parcellation tasks.
  • A parcellation map similarity loss and enforced longitudinal consistency for improved feature representation.

Main Results:

  • Significant improvements in registration and parcellation accuracy compared to existing methods.
  • Enhanced longitudinal consistency in tracking brain surface changes.
  • Superior performance, especially in small and challenging cortical regions.

Conclusions:

  • The proposed semi-supervised framework effectively addresses limitations of existing methods for longitudinal cortical surface analysis.
  • The method provides more robust and accurate registration and parcellation, crucial for studying brain development and disease progression.
  • This approach offers a significant advancement for longitudinal neuroimaging studies.