Guidewire Segmentation

Guidewire segmentation in medical imaging focuses on automatically identifying and outlining guidewires within X-ray or angiogram sequences during endovascular procedures. Current research emphasizes real-time performance using deep learning architectures like U-Net variations and YOLO, often incorporating techniques like reward shaping in reinforcement learning for autonomous navigation or weakly-supervised learning to reduce annotation burden. Accurate and efficient guidewire segmentation improves visualization for clinicians, facilitates robot-assisted interventions, and potentially reduces procedure times and radiation exposure.