首个动态手术图谱数据集，助力白内障手术精细化分析
CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery
- 构建包含工具-组织交互的动态场景图结构
- 支持手术阶段与操作技术的精准识别
- 适合医疗AI、智能手术系统研发者使用
白内障手术流程复杂，涉及器械、解剖结构与操作技术间的多重交互。现有数据集多聚焦单一任务如器械检测或阶段分割，缺乏对实体间语义关系的时序刻画。本文提出首个结构化标注的白内障手术场景图数据集（CAT-SG），涵盖工具-组织交互、操作变体及时间依赖关系。通过引入细粒度语义关联，实现手术流程的全局建模，显著提升阶段与技术识别精度。同时提出新型场景图生成模型CatSGG，优于现有方法。该数据集可推动人工智能在手术训练、实时决策支持与流程分析中的应用，助力临床智能化系统发展。
原文摘要 · Abstract (English)
Understanding the intricate workflows of cataract surgery requires modeling complex interactions between surgical tools, anatomical structures, and procedural techniques. Existing datasets primarily address isolated aspects of surgical analysis, such as tool detection or phase segmentation, but lack comprehensive representations that capture the semantic relationships between entities over time. This paper introduces the Cataract Surgery Scene Graph (CAT-SG) dataset, the first to provide structured annotations of tool-tissue interactions, procedural variations, and temporal dependencies. By incorporating detailed semantic relations, CAT-SG offers a holistic view of surgical workflows, enabling more accurate recognition of surgical phases and techniques. Additionally, we present a novel scene graph generation model, CatSGG, which outperforms current methods in generating structured surgical representations. The CAT-SG dataset is designed to enhance AI-driven surgical training, real-time decision support, and workflow analysis, paving the way for more intelligent, context-aware systems in clinical practice.
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