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arXiv 2607.16787cs.CV

HTT-Net:用于手术视频阶段识别的分层文本引导过渡建模

HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognition

Kunjie Deng, Jinghui Zhang, Weidong Chen, Ganbin Li, Xiangjun Lyu, Zhendong Mao, Yingchi Yang

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中文总结 AI 辅助

针对手术视频阶段识别难题,提出HTT-Net,通过构建分层手术语义记忆,利用过渡感知片段构建和校准方法,引入结构化手术语义知识,实验验证其能有效实现手术视频的稳健阶段识别。

中文摘要 AI 辅助

手术视频阶段识别是计算机辅助干预中的一项基础任务,能支持工作流程理解、术中指导和手术质量评估。尽管近期视觉-时间模型取得了进展,但由于局部视觉模糊、瞬态预测噪声和程序语义利用不足,准确且时间连贯的阶段识别仍具挑战。为此提出HTT-Net,将结构化手术语义知识引入阶段感知片段构建和语义细化。构建分层手术语义记忆,基于此提出过渡感知片段构建(TAS-Con)和过渡感知片段校准(TAS-Calib)。在Cholec80和LCRS-100上的实验证明了HTT-Net在手术视频阶段识别中的有效性。

英文摘要

Surgical video phase recognition is a fundamental task in computer-assisted intervention, supporting workflow understanding, intraoperative guidance, and surgical quality assessment. Although recent visual-temporal models have achieved promising progress, accurate and temporally coherent phase recognition remains challenging due to local visual ambiguity, transient prediction noise, and insufficient use of procedural semantics. To address these challenges, we propose HTT-Net, a Hierarchical Text-guided Transition modeling Network for surgical video phase recognition. The key idea is to introduce structured surgical semantic knowledge into phase-aware segment construction and semantic refinement. Specifically, we construct a hierarchical surgical semantic memory with intra-phase descriptions, inter-phase transition descriptions, and fine-grained semantic units. Based on this memory, the proposed Transition-Aware Segment Construction (TAS-Con) organizes frame-level evidence into coherent segment representations and handles boundary clips with inter-phase transition descriptions. Furthermore, we introduce Transition-Aware Segment Calibration (TAS-Calib), which calibrates phase-aware segment representations through hierarchical surgical semantics and improves discrimination under visual ambiguity without dense frame-level vision-language fusion. Experiments on Cholec80 and LCRS-100 demonstrate the effectiveness of HTT-Net for robust surgical video phase recognition.

发表机构

  • School of Information Science and Technology, University of Science and Technology of China(中国科学技术大学信息科学技术学院)
  • Department of General Surgery, Beijing Friendship Hospital, Capital Medical University(首都医科大学附属北京友谊医院普通外科)
  • Chinese PLA General Hospital(中国人民解放军总医院)

机构由 AI 辅助整理,请以论文原文为准。

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