基于SAM2的白内障手术视频分割模型，支持实时高精度分割与高效标注。
CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotation
- 基于SAM2改进，适配眼科前段手术视频实时分割
- 零样本泛化至青光眼手术，跨术式通用性强
- 结合稀疏提示与视频掩码传播，标注效率提升显著
我们提出CataractSAM-2，是Meta Segment Anything Model 2的领域自适应扩展，专为白内障眼科手术视频实现高精度实时语义分割。该模型处于计算机视觉与医疗机器人交叉领域，可支持机器人辅助及计算机引导手术系统的术中感知。为减轻人工标注负担，我们引入一种交互式标注框架，结合稀疏提示与视频掩码传播技术，显著降低标注时间，推动高质量真值掩码的规模化生成，加速眼科前段手术数据集构建。我们还验证了模型在青光眼小梁切除术上的强零样本泛化能力，证明其跨术式适用性及更广泛外科应用潜力。训练好的模型与标注工具已开源，确立CataractSAM-2作为扩展前段眼科手术数据集、推进实时AI驱动医疗机器人与手术视频理解的基石。
原文摘要 · Abstract (English)
We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and computer-guided surgical systems. Furthermore, to alleviate the burden of manual labeling, we introduce an interactive annotation framework that combines sparse prompts with video-based mask propagation. This tool significantly reduces annotation time and facilitates the scalable creation of high-quality ground-truth masks, accelerating dataset development for ocular anterior segment surgeries. We also demonstrate the model's strong zero-shot generalization to glaucoma trabeculectomy procedures, confirming its cross-procedural utility and potential for broader surgical applications. The trained model and annotation toolkit are released as open-source resources, establishing CataractSAM-2 as a foundation for expanding anterior ophthalmic surgical datasets and advancing real-time AI-driven solutions in medical robotics, as well as surgical video understanding.
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