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. Particularly, 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 will be publicly available upon acceptance of the
paper.

近年来，计算机辅助干预和术后手术视频分析的领域正在被深度学习技术所重塑，这些技术取得了外科医生技能、手术室管理和整体手术结果方面的显著进展。本文介绍了最大的白内障手术视频数据集，该数据集满足构建计算机化手术工作流分析和检测白内障手术后异常的多样性需求。我们通过评估白内障手术视频中的跨领域器械分割性能，开展了白内障手术领域适应性研究。数据集和注释将在文章被接受后公开。