CaDIS: Cataract dataset for surgical RGB-image segmentation

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to postoperative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts- semantic- segmentation2020.grand-challenge.org/ . (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

Video feedback is crucial for surgical procedures and scene understanding in computer assisted interventions. Semantic segmentation is essential for identifying and localizing surgical instruments and anatomical structures, with deep learning advancing techniques in recent years. This paper introduces a dataset for semantic segmentation of cataract surgery videos and benchmarks the performance of deep learning models.