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Updated: Aug 26, 2025

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Vox2Surf: Implicit Surface Reconstruction from Volumetric Data.

Yoonmi Hong1, Sahar Ahmad1, Ye Wu1

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

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|October 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a rapid implicit surface reconstruction method using signed distance functions (SDFs) for brain imaging. This approach significantly accelerates the analysis of brain structures from MRI data, improving efficiency for large-scale studies.

Keywords:
Cortical surface reconstructionDeep learningImplicit representation

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Surface reconstruction from MRI is crucial for brain analysis but is often slow and complex.
  • Current methods require extensive parameter tuning, limiting their use in large-scale neuroimaging studies.

Purpose of the Study:

  • To develop a fast and automated surface reconstruction method for volumetric brain imaging.
  • To enable efficient surface-based analysis for large-scale neuroimaging research.

Main Methods:

  • A novel implicit surface reconstruction technique learning a continuous-valued signed distance function (SDF).
  • Utilizing the marching cubes algorithm on the predicted SDF to generate 3D surfaces.
  • Concurrent prediction of multiple brain structures including parenchyma, white matter, pial surfaces, subcortical structures, and ventricles.

Main Results:

  • The proposed method achieves rapid surface reconstruction, completing analysis for 22 cortical and subcortical structures in under 20 minutes.
  • Demonstrated successful reconstruction of brain parenchyma, white matter, pial, subcortical, and ventricular surfaces.
  • Evaluation using Human Connectome Project data validated the method's speed and accuracy.

Conclusions:

  • This implicit surface reconstruction method offers a significant speed improvement over traditional techniques.
  • The fast and automated nature of this approach facilitates wider adoption of surface-based analysis in large-scale neuroimaging.
  • The method provides a robust tool for analyzing complex brain anatomy efficiently.