占用网络引导的自主机器人肾部分切除术

中文摘要 AI 辅助

自主软组织癌症手术一直局限于器官表面的干预，因为当前系统无法在解剖结构变形或被切割后感知并适应其变化。我们首次引入了视觉引导的自主系统，能够对肾部分切除术执行完整的肿瘤切除。我们的系统集成了条件占用网络，该网络完全在基于物理的仿真中训练，能够从单视角的部分点云推断出完整的3D解剖结构（肿瘤、边缘组织和肾脏）。这些占用网络即使在组织被切割和变形时也能维持术中跟踪，从而实现自适应规划与执行。手术平台结合了用于捕获表面点云的深度相机、用于电外科切割和基于真空的组织操作的双机械臂，以及用于肿瘤切除的自主控制策略。在开放式肾部分切除术设置下的患者来源水凝胶体模中，机器人执行了八次连续的自主肿瘤切除，共包含77次电外科切割，所有切割均实现了阴性手术切缘，平均绝对边缘误差为1.61 ± 0.48毫米。这项工作首次展示了在体模中进行监督式自主闭环、影像驱动、切缘阴性的肿瘤切除的基础。

英文摘要

Autonomous soft-tissue cancer surgery has been limited to interventions on organ surfaces, because current systems cannot perceive and adapt to anatomy once it deforms or is cut. We introduce the first vision-guided autonomous system capable of performing complete tumor resections for partial nephrectomy. Our system integrates conditional occupancy networks, trained entirely in a physics-based simulation, that infer full 3-D anatomy (tumor, margin tissue, and kidney) from single-view partial point clouds. These occupancy networks maintain intraoperative tracking even as tissue is cut and deformed, enabling adaptive planning and execution. The surgical platform combines a depth camera for capturing surface point clouds, dual robotic arms for electrosurgical cutting and vacuum-based tissue manipulation, and an autonomous control strategy for tumor resection. In patient-derived hydrogel phantoms under an open partial nephrectomy setting, the robot performed eight consecutive autonomous tumor resections comprising 77 electrosurgical cuts, with all cuts achieving negative surgical margins and 1.61 $\pm$ 0.48 mm mean absolute margin error. This work demonstrates, for the first time, a foundation for supervised autonomous closed-loop, imaging-driven, margin-negative tumor removal in phantoms.

发表机构

• Johns Hopkins University（约翰斯·霍普金斯大学）

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