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Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

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    This study introduces a novel framework for real-time guidewire endpoint localization in fluoroscopy images, crucial for computer-assisted percutaneous coronary intervention (PCI). The method achieves state-of-the-art accuracy and meets real-time performance demands.

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    Area of Science:

    • Medical Imaging
    • Computer-Assisted Surgery
    • Machine Learning

    Background:

    • Real-time localization of guidewire endpoints is essential for advancing computer-assisted percutaneous coronary intervention (PCI).
    • Current methods for localizing multiple guidewire endpoints in fluoroscopy images are limited.
    • Accurate endpoint localization enhances precision and safety in minimally invasive procedures.

    Purpose of the Study:

    • To develop and validate a novel framework for real-time multi-guidewire endpoint localization in fluoroscopy images.
    • To improve the accuracy and efficiency of guidewire endpoint detection for surgical guidance.
    • To demonstrate the generalizability of the proposed network to other surgical instrument localization tasks.

    Main Methods:

    • A two-stage framework was developed: guidewire detection using YOLOv3 with post-processing, followed by endpoint localization using a Segmentation Attention-hourglass (SA-hourglass) network.
    • The SA-hourglass network was designed for precise keypoint localization, adaptable to various surgical instruments.
    • The framework was evaluated on dedicated guidewire and retinal microsurgery datasets.

    Main Results:

    • The proposed framework achieved state-of-the-art localization results with a mean pixel error (MPE) of 2.20 pixels on the guidewire dataset and 5.30 pixels on the retinal microsurgery dataset.
    • The SA-hourglass network demonstrated generalizability, performing well on both guidewire and retinal microsurgery tasks.
    • The system achieved an inference rate of at least 20 FPS, exceeding the real-time requirements for fluoroscopy (6-12 FPS).

    Conclusions:

    • The developed framework provides accurate and real-time multi-guidewire endpoint localization in fluoroscopy images.
    • The SA-hourglass network is a robust tool for surgical instrument keypoint localization, applicable beyond guidewires.
    • This advancement supports the development of more sophisticated computer-assisted interventions, enhancing procedural outcomes.