  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 post-operative 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/.
