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Scientific Data · October 21, 2026

Labels for automated step recognition in cataract surgery for videos in the Cataract-1K dataset

Ibrahim Dehshini Ali, Nisarg A. Shah, Mingze Xia, Mike Peven, Nanthini Narayanan, Rishabh Nanawati

Nuren Zhaksylyk
Open via DOIPDF

Abstract

We provide surgical step annotations for all 1,000 videos in the publicly available Cataract-1K dataset of phacoemulsification cataract surgery. We manually labeled 386 videos using an annotation protocol with explicit boundary criteria for 10 surgical steps. Manual annotations demonstrated excellent inter-annotator reliability (intraclass correlation coefficient 0.994–1.000; mean intersection over union 0.980–0.993). Using the manually annotated videos, we trained five state-of-the-art deep learning models for surgical step recognition and localization and generated pseudo-labels for all 1,000 videos. We release the manual annotations, model-predicted pseudo-labels, and associated prediction probabilities through a publicly accessible repository. These data products, alongside our annotation protocol and statistical evaluation methodology, serve as a resource for developing context-aware systems for cataract surgery and as a guidance for annotation of steps and statistical evaluation of large surgical video datasets.

Surgical Simulation and TrainingMedical Image Segmentation TechniquesHuman Pose and Action Recognitioncataract surgeryvideo annotationsurgical video analysis

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