SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

Xiangning Zhang, Qingwei Zhang, Jinnan Chen, Chengfeng Zhou, Yaqi Wang, Zhengjie Zhang, Xiaobo Li, Dahong Qian

Abstract: Endoscopic Submucosal Dissection (ESD) is a minimally invasive procedure initially developed for early gastric cancer treatment and has expanded to address diverse gastrointestinal lesions. While computer-assisted surgery (CAS) systems enhance ESD precision and safety, their efficacy hinges on accurate real-time surgical phase recognition, a task complicated by ESD’s inherent complexity, including heterogeneous lesion characteristics and dynamic tissue interactions. Existing video-based phase recognition algorithms, constrained by inefficient temporal context modeling, exhibit limited performance in capturing fine-grained phase transitions and long-range dependencies. To overcome these limitations, we propose SPRMamba, a novel framework integrating a Mamba-based architecture with a Scaled Residual TranMamba (SRTM) block to synergize long-term temporal modeling and localized detail extraction. SPRMamba further introduces the Hierarchical Sampling Strategy to optimize computational efficiency, enabling real-time processing critical for clinical deployment. Evaluated on the ESD ESD385 dataset, the cholecystectomy Cholec80 dataset, and the hysterectomy AutoLaparo dataset, SPRMamba achieves state-of-the-art performance (87.70% accuracy on ESD385, +1.06% over prior methods), demonstrating robust generalizability across surgical workflows. This advancement bridges the gap between computational efficiency and temporal sensitivity, offering a transformative tool for intraoperative guidance and skill assessment in ESD surgery. The code is accessible at https://github.com/Zxnyyyyy/SPRMamba.

External IDs:doi:10.1109/jsen.2025.3649151