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Updated: Jun 25, 2026

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

Distributed local MRF models for tissue and structure brain segmentation.

Benoit Scherrer1, Florence Forbes, Catherine Garbay

  • 1INSERM, 38043 Grenoble, France. benoit.scherrer@imag.fr

IEEE Transactions on Medical Imaging
|February 21, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel cooperative approach for segmenting magnetic resonance (MR) brain scans, improving accuracy for both tissue and subcortical structures. The method uses distributed local Markov random field (MRF) models for robust and efficient brain scan segmentation.

Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Accurate segmentation of magnetic resonance (MR) brain scans is essential for various clinical and research applications.
  • Current methods often employ sequential tissue and subcortical structure segmentation, which can be suboptimal.
  • Intensity nonuniformity and noise are common challenges in MR brain imaging.

Purpose of the Study:

  • To develop a cooperative framework for simultaneous tissue and subcortical structure segmentation in MR brain scans.
  • To improve the accuracy and robustness of MR brain scan segmentation.
  • To reduce computational cost and handle intensity nonuniformity without bias field correction.

Main Methods:

  • A distributed set of local and cooperative Markov random field (MRF) models was employed.

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

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Published on: January 7, 2019

  • Tissue segmentation involved partitioning the volume and estimating local MRFs cooperatively.
  • Subcortical structure segmentation integrated localization constraints from a priori anatomical information, working jointly with tissue segmentation.

    • Main Results:

    • The proposed framework demonstrated good results on both phantom and real 3 T brain scans.
    • The method showed robustness to intensity nonuniformity and noise.
    • The approach achieved a low computational cost.

    Conclusions:

    • The cooperative segmentation framework effectively integrates tissue and subcortical structure segmentation.
    • The combination of local MRF models and anatomical knowledge offers a powerful and promising approach for MR brain scan segmentation.
    • This method provides an efficient and robust solution for MR brain image analysis.