arXiv 2602.206362026-02-24cs.CVcs.AI

SurgAtt-Tracker: 通过时间提案重排和运动感知细化实现在线手术注意力跟踪

SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement

Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学电子工程系）；Division of Gastrointestinal Surgery, Shenzhen People's Hospital, China（深圳人民医院胃肠外科）；College of Mechatronics and Control Engineering, Shenzhen University, China（深圳大学机电与控制工程学院）；Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China（香港大学机械工程系）；Department of Electronic and Electrical Engineering, Southern University of Science and Technology（南方科技大学电子与电气工程系）

Rulin Zhou, Guankun Wang, An Wang, Yujie Ma, Lixin Ouyang, Bolin Cui, Junyan Li, Chaowei Zhu, Mingyang Li, Ming Chen, Xiaopin Zhong, Peng Lu, Jiankun Wang, Xian… 展开作者

Rulin Zhou, Guankun Wang, An Wang, Yujie Ma, Lixin Ouyang, Bolin Cui, Junyan Li, Chaowei Zhu, Mingyang Li, Ming Chen, Xiaopin Zhong, Peng Lu, Jiankun Wang, Xianming Liu, Hongliang Ren

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AI总结：

SurgAtt-Tracker通过时间提案重排和运动感知细化实现手术注意力跟踪，提供帧级视野指导以支持机器人规划和自动摄像头控制。

摘要：

准确且稳定的视野（FoV）指导对于安全高效的微创手术至关重要，但现有方法往往将视觉注意力估计与下游摄像头控制混淆，或依赖直接以物体为中心的假设。在本工作中，我们将手术注意力跟踪视为一个时空学习问题，并将外科医生的注意力建模为密集的注意力热图，从而实现连续且可解释的帧级视野指导。我们提出了SurgAtt-Tracker，一个整体框架，通过利用时间一致性通过提案级重排和运动感知细化来稳健地跟踪手术注意力，而不是直接回归。为了支持系统的训练和评估，我们引入了SurgAtt-1.16M，一个大规模基准，具有临床基础的标注协议，能够跨手术和机构进行全面的热图注意力分析。在多个手术数据集上的广泛实验表明，SurgAtt-Tracker在遮挡、多仪器干扰和跨领域设置下均能持续实现最先进的性能和强大的鲁棒性。除了注意力跟踪外，我们的方法还提供了一个帧级视野指导信号，可以直接支持下游机器人的视野规划和自动摄像头控制。

英文摘要：

Accurate and stable field-of-view (FoV) guidance is critical for safe and efficient minimally invasive surgery, yet existing approaches often conflate visual attention estimation with downstream camera control or rely on direct object-centric assumptions. In this work, we formulate surgical attention tracking as a spatio-temporal learning problem and model surgeon focus as a dense attention heatmap, enabling continuous and interpretable frame-wise FoV guidance. We propose SurgAtt-Tracker, a holistic framework that robustly tracks surgical attention by exploiting temporal coherence through proposal-level reranking and motion-aware refinement, rather than direct regression. To support systematic training and evaluation, we introduce SurgAtt-1.16M, a large-scale benchmark with a clinically grounded annotation protocol that enables comprehensive heatmap-based attention analysis across procedures and institutions. Extensive experiments on multiple surgical datasets demonstrate that SurgAtt-Tracker consistently achieves state-of-the-art performance and strong robustness under occlusion, multi-instrument interference, and cross-domain settings. Beyond attention tracking, our approach provides a frame-wise FoV guidance signal that can directly support downstream robotic FoV planning and automatic camera control.

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