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    This study introduces an efficient method for matching coronary arteries to a standard anatomical model using geodesic paths. This automated labeling speeds up physician reporting and shows promise compared to existing techniques.

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    Area of Science:

    • Medical imaging
    • Computational anatomy
    • Cardiovascular research

    Background:

    • Accurate labeling of coronary arteries is crucial for diagnosing cardiovascular diseases.
    • Manual labeling is time-consuming and prone to inter-observer variability.
    • Automating coronary artery labeling can significantly improve reporting efficiency for physicians.

    Purpose of the Study:

    • To develop an efficient computational method for matching coronary artery trees to a standard anatomical model.
    • To improve the accuracy and speed of automated coronary artery labeling.
    • To address challenges in matching, such as missing arterial branches.

    Main Methods:

    • Utilized recent advancements in unique geodesic paths between tree shapes.
    • Employed Dijkstra's algorithm for efficient computation of geodesic paths.
    • Incorporated relative positioning of cardiac structures to enhance efficiency and accuracy.
    • Developed a methodology to handle missing side branches during the matching process.

    Main Results:

    • The proposed approach demonstrates efficient computation of geodesic paths.
    • The method effectively accounts for missing side branches in coronary artery trees.
    • Results show promise and comparable or improved performance against recent work for most labels.
    • Successfully applied the method for labeling 8 additional coronary artery segments.

    Conclusions:

    • The developed method offers an efficient and accurate solution for automated coronary artery labeling.
    • This technique has the potential to significantly expedite the reporting process for physicians.
    • The approach shows robustness in handling anatomical variations like missing branches.