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Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI.

Zhihao Chen1, Alain Lalande2, Michel Salomon1

  • 1FEMTO-ST Institute, UMR6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 5, 2021
PubMed
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This summary is machine-generated.

This study introduces an automated method using Convolutional Neural Networks (CNNs) for segmenting myocardial infarction in cardiac MRI scans. The CNN approach offers a faster and more consistent alternative to manual annotation for assessing heart disease severity.

Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Delayed Enhancement cardiac MRI (DE-MRI) is crucial for diagnosing myocardial diseases.
  • Manual annotation of scar and myocardium in DE-MRI is time-consuming and prone to variability.
  • Accurate quantification of myocardial infarction is essential for disease severity assessment.

Purpose of the Study:

  • To develop an automated segmentation approach for myocardial infarction on the left ventricle using DE-MRI.
  • To leverage Convolutional Neural Networks (CNNs) for efficient and accurate scar segmentation.
  • To compare the proposed method against manual segmentation and existing automated techniques.

Main Methods:

  • A two-stage CNN model was designed for segmenting myocardial infarction from short-axis DE-MRI.
Keywords:
Adaptive frameworkCNNDE-MRIMyocardial infarctionSemantic segmentation

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  • The first CNN model identifies the myocardium contour.
  • The second CNN model segments the infarction area within the myocardium.

    • Main Results:

    • The proposed CNN-based method achieved satisfying segmentation results on a dataset of 904 DE-MRI slices.
    • The automated segmentation demonstrated improved consistency compared to manual intra-observer and inter-observer variations.
    • Performance was evaluated against Gaussian Mixture Model-based automatic segmentation.

    Conclusions:

    • Automated CNN-based segmentation offers a promising solution for quantifying myocardial infarction in DE-MRI.
    • This approach can reduce the time and variability associated with manual annotation.
    • The method holds potential for improving the efficiency and accuracy of cardiovascular disease assessment.