In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons’ skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.

近年来，深度学习技术极大地重塑了计算机辅助干预和术后手术视频分析的格局，导致外科医生的技能、手术室管理和整体手术结果取得了显着进步。然而，深度学习驱动的外科技术的进步在很大程度上依赖于大规模数据集和注释。特别是，手术场景理解和相位识别是计算机辅助手术和白内障手术视频术后评估领域的关键支柱。在这种背景下，我们提出了最大的白内障手术视频数据集，该数据集满足了构建计算机化手术工作流程分析和检测白内障手术术后异常情况的各种要求。我们通过对用于相位识别和手术场景分割的几种最先进的神经网络架构的性能进行基准测试来验证注释的质量。此外，我们通过评估白内障手术视频中的跨域仪器分割性能，启动了白内障手术中仪器分割的域适应研究。数据集和注释在 Synapse 中公开可用。