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Updated: Jul 8, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
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A novel framework for differentiating vessel-like objects in coronarography images.

Witold Serwatka, Katarzyna Heryan, Joanna Sorysz

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a novel algorithm to refine coronary artery segmentation, removing artifacts like catheters. This 99% accurate method enhances AI-driven diagnosis for Coronary Artery Disease, improving patient care.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiovascular Disease

    Background:

    • Coronary Artery Disease (CAD) is a leading global cause of mortality.
    • Limited access to specialized care necessitates automated diagnostic tools.
    • Current coronary angiography segmentation methods struggle with artifacts, hindering automated diagnosis.

    Purpose of the Study:

    • To develop a post-segmentation refinement algorithm for precise coronary artery segmentation.
    • To eliminate vessel-like artifacts from segmentation results.
    • To enable automated AI-supported diagnosis workflows for CAD.

    Main Methods:

    • A two-step refinement algorithm using XGBoost Classifier and a neighborhood filter.
    • Extraction of image features from binary coronary artery segmentation.
    • Utilizing Tamura features for artifact differentiation.

    Main Results:

    • The algorithm achieves 99% accuracy in differentiating coronary arteries from artifacts.
    • Successfully removes artifacts like catheters and stitches from segmentation.
    • Enables automated quantitative evaluation of lesions in coronary angiography.

    Conclusions:

    • The proposed algorithm significantly improves coronary artery segmentation accuracy.
    • Facilitates the automation of AI-supported diagnosis for CAD.
    • Potential for application in other medical imaging domains for artifact removal.