2025/06/26 by Felix Holm, Gözde Ünver, Holm, Felix +5 · 1 citation
Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Intraocular Surgery and Lenses #Machine Learning (cs.LG) #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2506.21813
openalex publication_date 2025/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.