# Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures

arXiv: https://arxiv.org/abs/2507.00051 · HTML: https://mlsanity.com/paper/2507.00051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Learning to Filter: Siamese Relation Network for Robust Tracking](https://mlsanity.com/paper/2104.00829/) (2021)
- [Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review](https://mlsanity.com/paper/2305.04027/) (2023)
- [Simultaneous Denoising and Localization Network for Photoacoustic Target Localization](https://mlsanity.com/paper/2104.14713/) (2021)
- [MambaXCTrack: Mamba-based Tracker with SSM Cross-correlation and Motion Prompt for Ultrasound Needle Tracking](https://mlsanity.com/paper/2411.08395/) (2024)
- [VascularPilot3D: Toward a 3D fully autonomous navigation for endovascular robotics](https://mlsanity.com/paper/2405.09375/) (2024)
- [Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics](https://mlsanity.com/paper/2504.05330/) (2025)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [MrTrack: Register Mamba for Needle Tracking with Rapid Reciprocating Motion during Ultrasound-Guided Aspiration Biopsy](https://mlsanity.com/paper/2505.09450/) (2025)

## Abstract (arXiv)

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 ± 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 ± 0.695 mm and 0.148 ± 0.057 mm in two distinct experimental scenarios.