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.
@article{arxiv.2110.10965,
title = {2020 CATARACTS Semantic Segmentation Challenge},
author = {Imanol Luengo and Maria Grammatikopoulou and Rahim Mohammadi and Chris Walsh and Chinedu Innocent Nwoye and Deepak Alapatt and Nicolas Padoy and Zhen-Liang Ni and Chen-Chen Fan and Gui-Bin Bian and Zeng-Guang Hou and Heonjin Ha and Jiacheng Wang and Haojie Wang and Dong Guo and Lu Wang and Guotai Wang and Mobarakol Islam and Bharat Giddwani and Ren Hongliang and Theodoros Pissas and Claudio Ravasio and Martin Huber and Jeremy Birch and Joan M. Nunez Do Rio and Lyndon da Cruz and Christos Bergeles and Hongyu Chen and Fucang Jia and Nikhil KumarTomar and Debesh Jha and Michael A. Riegler and Pal Halvorsen and Sophia Bano and Uddhav Vaghela and Jianyuan Hong and Haili Ye and Feihong Huang and Da-Han Wang and Danail Stoyanov},
journal= {arXiv preprint arXiv:2110.10965},
year = {2022}
}