SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba
This work addresses the problem of accurate real-time surgical phase recognition for ESD procedures, which is critical for enhancing precision and safety in computer-assisted surgery systems, representing a domain-specific advancement with incremental improvements.
The paper tackles surgical phase recognition for Endoscopic Submucosal Dissection (ESD) by proposing SPRMamba, a framework that integrates Mamba-based architecture with a Scaled Residual TranMamba block and Hierarchical Sampling Strategy, achieving state-of-the-art performance with 87.64% accuracy on the ESD385 dataset (+1.0% over prior methods) and demonstrating robust generalizability on the Cholec80 benchmark.
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 ESD385 dataset and the cholecystectomy benchmark Cholec80, SPRMamba achieves state-of-the-art performance (87.64% accuracy on ESD385, +1.0% 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.