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Surface Foliation Based Brain Morphometry Analysis.

Chengfeng Wen1, Na Lei2, Ming Ma1

  • 1Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy : 4Th International Workshop, MBIA 2019, and 7Th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17
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Summary
This summary is machine-generated.

This study introduces a new brain surface analysis method using surface foliation theory. This approach effectively distinguishes Alzheimer's disease patients from healthy controls using intrinsic geometric features.

Keywords:
Alzheimer diseaseBrain morphometryshape classificationsurface foliation

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Area of Science:

  • Neuroimaging
  • Computational Geometry
  • Medical Image Analysis

Background:

  • Brain morphometry is crucial for understanding neurological conditions.
  • Current methods may lack intrinsic geometric features for robust analysis.
  • Surface foliation theory offers a novel geometric framework.

Purpose of the Study:

  • To develop a novel brain surface morphometry analysis method using surface foliation theory.
  • To extract intrinsic and informative geometric features from cortical surfaces.
  • To evaluate the method's efficacy in classifying Alzheimer's disease patients.

Main Methods:

  • Constructing finite foliations on brain cortical surfaces with landmark curves.
  • Utilizing Strebel differentials and harmonic maps for surface decomposition.
  • Mapping topological cylinders to standard cylinders to extract height and circumference parameters.

Main Results:

  • Novel intrinsic surface features were successfully computed.
  • The method demonstrated rigorous, geometric, and automatic analysis.
  • Experimental classification between Alzheimer's disease and healthy controls showed high efficacy.

Conclusions:

  • Surface foliation theory provides a powerful new tool for brain morphometry.
  • The proposed features are intrinsic, informative, and geometrically rigorous.
  • The method shows significant potential for clinical applications in neurodegenerative disease research.