A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework

Sheng Cao; Shuxiang Guo; Yonggan Yan; Jian Guo; Chunying Li; Bin Wang; Mingchao Ding; Yongxin Zhang; Yongwei Zhang; Pengfei Yang; Jianmin Liu

arXiv · 2610.21481

A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework

Sheng Cao·Shuxiang Guo·Yonggan Yan·Jian Guo·Chunying Li·Bin Wang·Mingchao Ding·Yongxin Zhang·Yongwei Zhang·Pengfei Yang·Jianmin Liu

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Abstract

In endovascular interventions, physicians rely on planar DSA and X-ray fluoroscopy to interpret vessel anatomy and device motion, yet converting these images into feedback for autonomous navigation remains difficult. A perception and control method is formulated for two-dimensional image feedback. Vessel masks are converted into a labeled centerline graph, and device-tip estimates are projected onto a planned path to obtain progress, deviation, and direction. A rule-based controller uses these quantities for delivery, branch selection, probing, rollback, and recovery. The method was evaluated on a physical dual-device robotic platform in 65 closed-loop runs using simulator-rendered planar images across three paths. All runs reached at least 0.95 maximum observed guidewire progress; two runs (3.1%) met the prespecified failure rule of three or more autonomous stop events. The results establish route-level performance on the tested platform; clinical image sequences and broader anatomy remain to be evaluated.

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Sheng Cao, Shuxiang Guo, Yonggan Yan, Jian Guo, Chunying Li, Bin Wang, Mingchao Ding, Yongxin Zhang, Yongwei Zhang, Pengfei Yang, Jianmin Liu. 2026-10-15. A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework. https://arxiv.org/abs/2610.21481

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