通过动作和体态识别手术人员，实现跨场景长期追踪。
Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
- 以个体运动模式和身体特征建模，替代传统角色分类。
- 跨环境准确率达75.27%，比现有方法高12%。
- 适合研究手术团队协作与手术室空间利用的学者。
手术领域模型通过自动预测医护人员角色来优化工作流程。然而，越来越多证据表明团队熟悉度与个体差异会影响手术结果。本文提出一种以人员为中心的新建模方法，通过分析每位成员独特的运动模式与物理特征，实现跨多台手术的长期人员追踪与分析。为应对不同医疗机构间的差异，我们构建了一个可泛化的重识别框架，将3D点云序列编码为个体特有的形状与关节运动特征。该方法在真实临床数据上达到86.19%的准确率，跨环境迁移时仍保持75.27%准确率，较现有方法提升12%。用于增强无标记人员追踪时，准确率提升超50%。通过在三个数据集上的广泛验证及新型工作流可视化技术的引入，本框架揭示了手术团队动态与空间利用的新见解，推动了手术流程与团队协作分析方法的发展。
原文摘要 · Abstract (English)
Surgical domain models improve workflow optimization through automated predictions of each staff member's surgical role. However, mounting evidence indicates that team familiarity and individuality impact surgical outcomes. We present a novel staff-centric modeling approach that characterizes individual team members through their distinctive movement patterns and physical characteristics, enabling long-term tracking and analysis of surgical personnel across multiple procedures. To address the challenge of inter-clinic variability, we develop a generalizable re-identification framework that encodes sequences of 3D point clouds to capture shape and articulated motion patterns unique to each individual. Our method achieves 86.19% accuracy on realistic clinical data while maintaining 75.27% accuracy when transferring between different environments - a 12% improvement over existing methods. When used to augment markerless personnel tracking, our approach improves accuracy by over 50%. Through extensive validation across three datasets and the introduction of a novel workflow visualization technique, we demonstrate how our framework can reveal novel insights into surgical team dynamics and space utilization patterns, advancing methods to analyze surgical workflows and team coordination.
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