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Related Experiment Video

Updated: Nov 11, 2025

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI
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Automatic Aortic Dissection Centerline Extraction Via Morphology-Guided CRN Tracker.

Jingliang Zhao, Qianjin Feng

    IEEE Journal of Biomedical and Health Informatics
    |March 23, 2021
    PubMed
    Summary
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    Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026

    This study presents an automated algorithm for extracting the centerline of aortic dissection (AD). The method utilizes convolutional regression networks and morphological properties, achieving high accuracy for improved AD diagnosis and treatment.

    Area of Science:

    • Medical Imaging
    • Computational Anatomy
    • Cardiovascular Research

    Background:

    • Aortic dissection (AD) centerline extraction is crucial for quantitative diagnosis and treatment.
    • Extracting AD centerlines is challenging, with limited quantitative evaluation methods.

    Purpose of the Study:

    • To develop a fully automatic algorithm for AD centerline extraction.
    • To improve the accuracy and stability of AD centerline tracking.

    Main Methods:

    • A topological model was designed to describe AD complexity.
    • Convolutional regression networks (CRNs) were trained to estimate centerline position, tangential vector, and scale.
    • Centerline continuity and a gradient-based penalty function enhanced tracking accuracy.
    • Automated vessel tracking was ensured by seed points from random regression and line clustering.

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    Main Results:

    • The algorithm achieved high overlapping ratios of 0.9610 on an AD database and 1.0000 on a public aortic database.
    • Tracked centerlines demonstrated close proximity to ground truth.
    • The method exhibited good stability with low average distance errors (1.4720 mm and 1.8748 mm).

    Conclusions:

    • The proposed fully automatic algorithm effectively extracts AD centerlines.
    • The method offers a robust and accurate solution for quantitative analysis in AD disease.
    • This approach has significant potential for clinical applications in AD diagnosis and treatment planning.