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Related Experiment Video

Updated: May 11, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Robust Diffeomorphic Mapping via Geodesically Controlled Active Shapes.

Daniel J Tward1, Jun Ma, Michael I Miller

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

International Journal of Biomedical Imaging
|May 22, 2013
PubMed
Summary
This summary is machine-generated.

Geodesically controlled diffeomorphic active shapes (GDAS) advance large deformation mapping by integrating conservation laws. This method offers robust segmentation of subcortical structures like the hippocampus from MRI data.

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Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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Last Updated: May 11, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Geometric deep learning

Background:

  • Large deformation diffeomorphic metric mapping (LDDMM) is crucial for analyzing anatomical changes.
  • Active shape models offer deformable segmentation but often lack rigorous mathematical underpinnings for large deformations.
  • Integrating conservation laws into active shape models provides a more robust and principled approach to diffeomorphic mapping.

Purpose of the Study:

  • To introduce Geodesically Controlled Diffeomorphic Active Shapes (GDAS) that incorporate conservation laws of LDDMM.
  • To apply GDAS for robust segmentation of subcortical structures, specifically the hippocampus, using parameterized surface representations.
  • To formulate segmentation as an energy minimization problem solvable via variational methods and gradient descent.

Main Methods:

  • GDAS evolution is parameterized by initial momentum in the tangent space of template surfaces, with dimensionality reduction via Principal Component Analysis.
  • Segmentation is achieved using three data attachment terms: surface matching, landmark matching, and inside-outside modeling from T1 MR imaging.
  • A variational solution is derived for the energy minimization problem, employing a gradient descent strategy for numerical optimization.

Main Results:

  • The study demonstrates the application of GDAS for segmenting hippocampus using template surfaces and multiple data attachment terms.
  • The GDAS framework is shown to be robust, particularly in the landmark matching scenario.
  • Comparison with an existing diffeomorphic landmark matching algorithm validates the performance of the proposed GDAS method in a large neuroanatomical study context.

Conclusions:

  • GDAS provides a principled framework for diffeomorphic active shape modeling by incorporating conservation laws.
  • The proposed method enables robust and accurate segmentation of subcortical structures from MRI data.
  • GDAS shows significant promise for applications in large-scale neuroanatomical studies and medical image analysis.