To propose a deep learning-based catheter and guidewire segmentation and tip tracking method leveraging the CathAction dataset for accurate, real-time device localization under clinically challenging conditions, addressing the limitations of current tracking methods.
Key Findings:
Novel tracking pipeline improves localization under low contrast and occlusion, potentially enhancing procedural safety.
Modern segmentation architectures achieve better performance when embedded in the tracking framework compared to existing benchmarks, indicating a shift in approach for future studies.
Proposed method validated across in vitro phantom, in vivo animal, and clinical fluoroscopic data, showcasing its versatility.
Interpretation:
The study demonstrates the feasibility of real-time catheter tip tracking, highlighting the potential for AI-driven endovascular navigation systems.
Limitations:
Performance evaluation primarily based on the CathAction dataset; generalizability to other datasets not fully established, suggesting the need for broader testing.
Limited exploration of real-time tracking in highly variable clinical environments, indicating areas for future research.
Conclusion:
The integration of advanced segmentation models into a tracking framework significantly enhances catheter tip localization, paving the way for improved autonomous navigation in endovascular procedures, which could lead to better patient outcomes.
by Harry Robertshaw, Yanghe Hao, Weiyuan Deng, Benjamin Jackson, S. M. Hadi Sadati, Nikola Fischer, Tom Vercauteren, Alejandro Granados, Thomas C. Booth
