2020 CATARACTS Semantic Segmentation Challenge

TL;DR

The 2020 CATARACTS Semantic Segmentation Challenge provided a new dataset with pixel-wise annotations for cataract surgery images to evaluate segmentation algorithms, advancing surgical scene understanding.

Contribution

This paper introduces a large, annotated dataset and a challenge for semantic segmentation in cataract surgery, fostering development of automated surgical scene analysis.

Findings

Participation in the challenge highlighted current segmentation capabilities.

Benchmark results established baseline performance for future research.

The dataset enabled detailed evaluation of anatomy and instrument segmentation algorithms.

Abstract

Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set.