SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

• URL: http://arxiv.org/abs/2409.12108v2
• Date: Wed, 21 May 2025 08:18:46 GMT
• Title: SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba
• Authors: Xiangning Zhang, Qingwei Zhang, Jinnan Chen, Chengfeng Zhou, Yaqi Wang, Zhengjie Zhang, Xiaobo Li, Dahong Qian,
• Abstract summary: We present SPRMamba, a novel framework for real-time surgical phase recognition.<n>It integrates a Mamba architecture with a Scaled Residual TranMamba block to synergize temporal modeling and localized detail extraction.<n>It achieves state-of-the-art performance (87.64% accuracy on ESD385, +1.0% over prior methods) demonstrating robust generalizability across surgical procedures.
• Score: 6.531066045206769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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