用视觉引导实现气管肿瘤自动切除，精准无穿孔。
Autonomous Vision-Guided Resection of Central Airway Obstruction
- 用五次多项式建模气管表面，规划安全轨迹。
- 5次实验均成功清除阻塞，肿瘤切除率超90%。
- 适合微创手术自动化研究者参考。
现有气管肿瘤切除方法精度不足，机器人技术为自主切除带来新可能。本文提出一种视觉引导的自主切除方案，通过五次多项式模型构建气管表面，规划工具路径；采用定制Faster R-CNN分割流程识别气管与肿瘤边界；基于手持手术演示优化电刀角度，路径规划保持距气管表面1 mm安全距离。在五个离体动物组织模型上验证，所有实验均成功清除气道阻塞，未发生气管穿孔，肿瘤体积切除率超过90%。结果表明该自主切除平台具备可行性，为未来微创自主切除技术发展奠定基础。
原文摘要 · Abstract (English)
Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool trajectories, while a custom Faster R-CNN segmentation pipeline identifies the trachea and tumor boundaries. The electrocautery tool angle is optimized using handheld surgical demonstrations, and trajectories are planned to maintain a 1 mm safety clearance from the tracheal surface. We validated the workflow successfully in five consecutive experiments on ex-vivo animal tissue models, successfully clearing the airway obstruction without trachea perforation in all cases (with more than 90% volumetric tumor removal). These results support the feasibility of an autonomous resection platform, paving the way for future developments in minimally-invasive autonomous resection.
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