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Trajectory Grouping With Curvature Regularization for Tubular Structure Tracking.

Li Liu, Da Chen, Minglei Shu

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    This study introduces a new method for extracting tubular structure centerlines using minimal paths and perceptual grouping. The approach improves accuracy in complex medical images by considering trajectories and curvature.

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    Area of Science:

    • Computer Vision
    • Medical Image Analysis

    Background:

    • Tubular structure tracking is vital in computer vision and medical imaging.
    • Minimal paths-based methods model structures as geodesic paths but struggle with complex images.

    Purpose of the Study:

    • To develop a novel minimal paths-based model for minimally interactive tubular structure centerline extraction.
    • To address limitations of existing methods in handling complex tubular structures and backgrounds.

    Main Methods:

    • Combines a minimal paths-based model with a perceptual grouping scheme.
    • Utilizes curvature-penalized geodesic paths and prescribed tubular trajectories.
    • Employs a graph-based path searching scheme for global optimality.

    Main Results:

    • The proposed model demonstrates superior performance compared to state-of-the-art methods.
    • Successfully extracts centerlines from both synthetic and real medical images.
    • Overcomes challenges like shortcuts and short branch combinations.

    Conclusions:

    • The new model offers an effective solution for tubular structure centerline extraction.
    • Integrates local smoothness priors with global path searching for robust results.
    • Shows significant outperformance in complex imaging scenarios.