Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase
  Recognition, and Irregularity Detection
Negin Ghamsarian Yosuf El-Shabrawi Sahar Nasirihaghighi Doris Putzgruber-Adamitsch Martin Zinkernagel Sebastian Wolf Klaus Schoeffmann Raphael Sznitman
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.