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.

翻译：机器人辅助内镜黏膜下剥离术（ESD）通过先进的机器人器械和双手操作提供更全面的视野，从而提升了剥离效率与精度，改善了手术流程。准确预测剥离轨迹对于优化决策、减少术中误差以及改进手术培训至关重要。然而，由于肿瘤边界多变和视觉条件动态变化，预测这些轨迹具有挑战性。为解决此问题，我们创建了包含 $1849$ 个短视频片段的 ESD 轨迹与基于置信度图的安全边界（ETSM）数据集，重点关注双机械臂机器人系统的黏膜下剥离过程。我们还提出了一种框架，将最优剥离轨迹预测与基于置信度图的安全边界相结合，为 ESD 手术提供更安全、更智能的决策工具，以最小化手术风险。此外，我们提出了基于回归的置信度图预测网络（RCMNet），该网络利用回归方法预测剥离区域的置信度图，从而划分不同级别的安全边界。我们通过三种不同的实验设置评估 RCMNet：域内评估、鲁棒性评估和域外评估。实验结果表明，我们的方法在基于置信度图的安全边界预测任务中表现优异，平均绝对误差（MAE）仅为 $3.18$。据我们所知，这是首次应用回归方法实现针对剥离区域不同安全级别划分的视觉引导研究。我们的方法通过提升预测准确性和增强剥离过程的安全性，弥补了当前研究的不足，在实践中显示出重要的临床意义。