Endovascular Intervention Tool Segmentation and Collision Detection

Le résumé fourni par la source

Cardiovascular disease remains one of the leading causes of mortality worldwide. Minimally invasive endovascular interventions have emerged as the standard of care due to reduced trauma, lower complication rates, and faster post-operative recovery compared to traditional open surgery. However, navigating catheters and guidewires through the vasculature demands highly specialized skills; unintended collisions with vessel walls can result in perforation, hemorrhage, or life-threatening complications. Although recent advances in surgical AI have shown promise, progress in endovascular assistance is limited by the absence of large, diverse, and clinically realistic datasets for benchmarking algorithmic performance.

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