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Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.

Jie Liu1, Hongzhi Xie2, Shuyang Zhang2

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

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|December 1, 2018
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Summary
This summary is machine-generated.

This study introduces a new method for simultaneously segmenting T2 and delayed enhancement cardiovascular magnetic resonance imaging (CMR) in myocardial infarction (MI) patients. The approach improves multi-sequence CMR analysis by addressing registration challenges.

Keywords:
Cardiovascular magnetic resonance imagingConditional generative adversarial networkCross constrained shapeMulti-sequence analysisShape discrepancy compensation

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Area of Science:

  • Medical Imaging Analysis
  • Cardiovascular Magnetic Resonance Imaging (CMR)

Background:

  • Delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) are crucial for myocardial infarction (MI) patient management.
  • Non-rigid registration between different CMR sequences presents a significant challenge, hindering multi-sequence image analysis.

Purpose of the Study:

  • To develop a novel approach for simultaneous segmentation of T2 and DE CMR images.
  • To enable robust multi-sequence CMR image analysis by overcoming registration limitations.

Main Methods:

  • A coupled level set method framework for unified multi-sequence image segmentation.
  • Cross-constrained sparse representation-based shape model with explicit myocardium shape discrepancy compensation.
  • Gaussian mixture model for intensity feature extraction and conditional generative adversarial network for automatic initialization.

Main Results:

  • Achieved promising Dice similarity coefficients for myocardium segmentation: 84.97±4.15% for T2 CMR and 78.13±6.22% for DE CMR.
  • Demonstrated the effectiveness of the approach in a cohort of 32 MI patients.

Conclusions:

  • The proposed method offers a viable solution for simultaneous T2 and DE CMR segmentation.
  • This work represents a significant step towards automated, multi-sequence CMR image analysis for improved MI patient care.