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Updated: May 3, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

Liang Zhao1, Wei Wu2, Jason J Corso3

  • 1Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY, USA. lzhao6@buffalo.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 8, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a semi-automatic brain tumor segmentation method for multi-channel MR images. The novel approach improves accuracy by using iterative optimization and structural trajectories, outperforming existing methods.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Accurate brain tumor quantification is crucial for prognosis.
  • Fully automatic segmentation methods struggle with tumor variability.
  • Clinical adoption of current methods is limited.

Purpose of the Study:

  • To develop a semi-automatic segmentation framework for multi-channel MR images.
  • To overcome limitations of fully automatic brain tumor segmentation.
  • To improve the clinical utility of brain tumor volume and growth quantification.

Main Methods:

  • A semi-automatic framework for multi-channel MR image segmentation.
  • Iterative multi-label Markov Random Field (MRF) optimization with hard constraints.
  • Utilizing structural trajectories for pixel correspondence between slices.

Main Results:

  • Demonstrated robustness and effectiveness on the 2012 MICCAI BRATS Challenge Dataset.
  • Achieved superior performance compared to baseline methods.
  • Validated the utility of the constrained MRF formulation for brain tumor segmentation.

Conclusions:

  • The proposed semi-automatic framework offers a viable solution for brain tumor segmentation.
  • The method effectively handles tumor variability without requiring prior models.
  • This approach shows promise for improved clinical prognostic measures.