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Related Experiment Video

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Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
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A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes.

Pietro Gori1, Olivier Colliot2, Linda Marrakchi-Kacem1

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Medical Image Analysis
|September 9, 2016
PubMed
Summary

This study introduces a Bayesian framework for creating shape atlases from complex data, improving automated parameter estimation and enabling group comparisons for medical research. The method enhances analysis of anatomical structures and fiber bundles.

Keywords:
AtlasBayesianComplexFiber bundleMorphometryMulti-objectShapeVarifolds

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Area of Science:

  • Computational anatomy
  • Medical image analysis
  • Statistical modeling

Background:

  • Atlas construction is crucial for understanding anatomical variations.
  • Existing methods often require manual parameter tuning.
  • Modeling complex shapes (surfaces and curves) presents challenges.

Purpose of the Study:

  • To develop a general Bayesian framework for multi-object shape atlas construction.
  • To automate parameter estimation for data-terms and deformation regularity.
  • To enable statistical comparison of shape variations across different groups.

Main Methods:

  • Modeling curve and surface meshes as Gaussian random varifolds.
  • Utilizing a finite-dimensional approximation space for probability density functions (PDFs).
  • Extending the framework for multi-group data with distinct deformation parameter distributions.

Main Results:

  • Automated estimation of parameters balancing data fidelity and deformation smoothness.
  • Estimation of a well-conditioned covariance matrix for deformation parameters.
  • Successful application to compare morphological differences between Gilles de la Tourette patients and controls using sub-cortical regions and white matter tracts.

Conclusions:

  • The proposed Bayesian framework offers a robust and automated approach to shape atlas construction.
  • It facilitates statistically sound comparisons of shape variations across distinct populations.
  • The method is integrated into the Deformetrica software for broader accessibility.