Endoscopic Submucosal Dissection (ESD) is a minimally invasive procedure initially designed for the treatment of early gastric cancer but is now widely used for various gastrointestinal lesions. Computer-assisted Surgery systems have played a crucial role in improving the precision and safety of ESD procedures, however, their effectiveness is limited by the accurate recognition of surgical phases. The intricate nature of ESD, with different lesion characteristics and tissue structures, presents challenges for real-time surgical phase recognition algorithms. Existing surgical phase recognition algorithms struggle to efficiently capture temporal contexts in video-based scenarios, leading to insufficient performance. To address these issues, we propose SPRMamba, a novel Mamba-based framework for ESD surgical phase recognition. SPRMamba leverages the strengths of Mamba for long-term temporal modeling while introducing the Scaled Residual TranMamba block to enhance the capture of fine-grained details, overcoming the limitations of traditional temporal models like Temporal Convolutional Networks and Transformers. Moreover, a Temporal Sample Strategy is introduced to accelerate the processing, which is essential for real-time phase recognition in clinical settings. Extensive testing on the ESD385 dataset and the cholecystectomy Cholec80 dataset demonstrates that SPRMamba surpasses existing state-of-the-art methods and exhibits greater robustness across various surgical phase recognition tasks.

本研究解决了内镜下黏膜下解剖手术（ESD）中手术阶段实时识别的准确性问题。提出的SPRMamba框架利用Mamba进行长期时间建模，同时引入了Scaled Residual TranMamba模块来捕获细微特征，显著提高了识别效果。实验结果显示，SPRMamba在不同的手术阶段识别任务中均优于现有的最佳方法，具有更强的鲁棒性。

SPRMamba：基于Mamba的内镜下黏膜下解剖手术阶段识别