Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, and improving surgical training. Nevertheless, predicting these trajectories is challenging due to variable tumor margins and dynamic visual conditions. To address this issue, we create the ESD Trajectory and Confidence Map-based Safety Margin (ETSM) dataset with $1849$ short clips, focusing on submucosal dissection with a dual-arm robotic system. We also introduce a framework that combines optimal dissection trajectory prediction with a confidence map-based safety margin, providing a more secure and intelligent decision-making tool to minimize surgical risks for ESD procedures. Additionally, we propose the Regression-based Confidence Map Prediction Network (RCMNet), which utilizes a regression approach to predict confidence maps for dissection areas, thereby delineating various levels of safety margins. We evaluate our RCMNet using three distinct experimental setups: in-domain evaluation, robustness assessment, and out-of-domain evaluation. Experimental results show that our approach excels in the confidence map-based safety margin prediction task, achieving a mean absolute error (MAE) of only $3.18$. To the best of our knowledge, this is the first study to apply a regression approach for visual guidance concerning delineating varying safety levels of dissection areas. Our approach bridges gaps in current research by improving prediction accuracy and enhancing the safety of the dissection process, showing great clinical significance in practice.

本研究解决了机器人辅助内窥镜下粘膜下层解剖（ESD）中解剖轨迹预测的困难，提升了手术决策的准确性和安全性。我们提出了ETSM数据集及RCMNet框架，将解剖轨迹预测与置信度映射结合，以优化安全边际预测，实验结果表明该方法显著提高了解剖安全性，临床应用潜力巨大。

ETSM: Automating Dissection Trajectory Suggestion and Confidence
  Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal
  Dissection

ETSM：自动化解剖轨迹建议与基于置信度映射的机器人辅助内窥镜下粘膜下层解剖安全边际预测