The increasing prevalence of prostate cancer has led to the widespread adoption of Robotic-Assisted Surgery (RAS) as a treatment option. Sentinel lymph node biopsy (SLNB) is a crucial component of prostate cancer surgery and requires accurate diagnostic evidence. This procedure can be improved by using a drop-in gamma probe, SENSEI system, to distinguish cancerous tissue from normal tissue. However, manual control of the probe using live gamma level display and audible feedback could be challenging for inexperienced surgeons, leading to the potential for missed detections. In this study, a deep imitation training workflow was proposed to automate the radioactive node detection procedure. The proposed training workflow uses simulation data to train an end-to-end vision-based gamma probe manipulation agent. The evaluation results showed that the proposed approach was capable to predict the next-step action and holds promise for further improvement and extension to a hardware setup.

翻译：前列腺癌发病率的持续上升促使机器人辅助手术（RAS）广泛用于其治疗。前哨淋巴结活检（SLNB）是前列腺癌手术的关键环节，需要精确的诊断依据。通过使用插入式伽马探头（SENSEI系统）区分癌变与正常组织，可提升该手术效果。然而，对经验不足的外科医生而言，仅依靠实时伽马强度显示与声音反馈进行手动探头操控存在挑战，可能导致漏检。本研究提出一种基于深度模仿学习的自动化放射性淋巴结检测流程，利用仿真数据训练端到端的视觉伽马探头操控智能体。评估结果表明，该方法能够预测下一步操作动作，并具备进一步改进及向硬件设备扩展的潜力。