Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy

2026-09-14

AI Summary

本文介绍了一种基于条件占用网络的视觉引导自主系统，用于解决软组织癌症手术中因组织变形或切割导致的感知和适应问题，实现完整的肿瘤切除。

Abstract

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.

Problem

• autonomous surgery
• soft-tissue cancer
• anatomy deformation
• partial nephrectomy
• occupancy networks

Innovation

• occupancy networks
• autonomous surgery
• partial nephrectomy
• tumor resection