SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

• URL: http://arxiv.org/abs/2409.12108v1
• Date: Wed, 18 Sep 2024 16:26:56 GMT
• Title: SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba
• Authors: Xiangning Zhang, Jinnan Chen, Qingwei Zhang, Chengfeng Zhou, Zhengjie Zhang, Xiaobo Li, Dahong Qian,
• Abstract summary: We propose SPRMamba, a novel Mamba-based framework for ESD surgical phase recognition. We show that SPRMamba surpasses existing state-of-the-art methods and exhibits greater robustness across various surgical phase recognition tasks.
• Score: 4.37495931705689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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