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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based

Zijun Gao1, Lu Wang2, Reza Soroushmehr2,3,4

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA. zijung@umich.edu.

BMC Medical Imaging
|January 20, 2022
PubMed
Summary

This study introduces a novel ensemble framework for segmenting coronary arteries in X-ray coronary angiography (XCA) images, improving computer-aided diagnosis of coronary artery disease (CAD). The method shows superior performance over deep learning models, enhancing patient care.

Keywords:
Deep learningEnsemble learningMedical image segmentationX-ray coronary angiography

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Diagnostics

Background:

  • Automated segmentation of coronary arteries is vital for computer-aided diagnosis and treatment planning of coronary artery disease (CAD).
  • Accurate delineation in X-ray coronary angiography (XCA) is challenging due to low signal-to-noise ratio and background interference.

Purpose of the Study:

  • To propose a novel ensemble framework for coronary artery segmentation in XCA images.
  • To enhance the accuracy and consistency of automated segmentation compared to existing deep learning methods.

Main Methods:

  • Developed an ensemble framework combining deep learning and filter-based features using Gradient Boosting Decision Tree (GBDT) and deep forest classifiers.
  • Constructed 37-dimensional feature vectors from multi-scale filtering responses and deep neural network feature maps.
  • Trained and tested the models on 130 XCA images, evaluating performance using precision, sensitivity, specificity, F1 score, AUROC, and IoU.

Main Results:

  • The best GBDT model achieved an F1 score of 0.874 and AUROC of 0.947.
  • The best deep forest model achieved an F1 score of 0.867 and AUROC of 0.95.
  • Both ensemble models demonstrated superior or comparable performance with lower standard deviations than common deep neural networks.

Conclusions:

  • The proposed feature-based ensemble method outperforms standard deep convolutional neural networks in coronary artery segmentation.
  • This method provides more consistent results, facilitating stenosis assessment and improving care for CAD patients.