Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
An ever-growing incorporation of AI solutions into clinical practices
enhances the efficiency and effectiveness of healthcare services. This paper
focuses on guidewire tip tracking tasks during image-guided therapy for
cardiovascular diseases, aiding physicians in improving diagnostic and
therapeutic quality. A novel tracking framework based on a Siamese network with
dual attention mechanisms combines self- and cross-attention strategies for
robust guidewire tip tracking. This design handles visual ambiguities, tissue
deformations, and imaging artifacts through enhanced spatial-temporal feature
learning. Validation occurred on 3 randomly selected clinical digital
subtraction angiography (DSA) sequences from a dataset of 15 sequences,
covering multiple interventional scenarios. The results indicate a mean
localization error of 0.421 $\pm$ 0.138 mm, with a maximum error of 1.736 mm,
and a mean Intersection over Union (IoU) of 0.782. The framework maintains an
average processing speed of 57.2 frames per second, meeting the temporal
demands of endovascular imaging. Further validations with robotic platforms for
automating diagnostics and therapies in clinical routines yielded tracking
errors of 0.708 $\pm$ 0.695 mm and 0.148 $\pm$ 0.057 mm in two distinct
experimental scenarios.
