SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

Xiangning Zhang, Jinnan Chen, Qingwei Zhang, Chengfeng Zhou, Zhengjie Zhang, Xiaobo Li, Dahong Qian
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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.
SPRMamba:使用 Mamba 进行内窥镜粘膜下剥离的手术阶段识别
内镜黏膜下剥离术(ESD)是一种微创手术,最初设计用于治疗早期胃癌,现在已被广泛用于治疗各种胃肠道病变。计算机辅助手术系统在提高 ESD 手术的精确性和安全性方面发挥了重要作用,但其有效性受到手术阶段准确识别的限制。ESD 的性质错综复杂,病变特征和组织结构各不相同,这给实时手术阶段识别算法带来了挑战。现有的手术阶段识别算法难以在基于视频的场景中有效捕捉时间背景,导致性能不足。为了解决这些问题,我们提出了基于 Mamba 的新型 ESD 手术相位识别框架 SPRMamba。SPRMamba 充分利用了 Mamba 在长期时空建模方面的优势,同时引入了 Scaled Residual TranMamba 块来增强对细粒度细节的捕捉,克服了传统时空模型(如时空卷积网络和变换器)的局限性。在ESD385数据集和胆囊切除术Cholec80数据集上进行的广泛测试表明,SPRMamba超越了现有的先进方法,在各种手术相位识别任务中表现出更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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