2020 CATARACTS Semantic Segmentation Challenge
/ Authors
Imanol Luengo, Maria Grammatikopoulou, R. Mohammadi, Christopher Walsh, C. Nwoye, Deepak Alapatt, N. Padoy, Zhen-Liang Ni, Chen-Chen Fan, Guibin Bian
and 29 more authors
Z. Hou, Heonjin Ha, Jiacheng Wang, Haojie Wang, D. Guo, Lu Wang, Guotai Wang, Mobarak Islam Hoque, Bharat Giddwani, Ren Hongliang, Theodoros Pissas, C. Huber, J. Birch, J. M. D. Rio, L. Cruz, C. Bergeles, Hongyu Chen, F. Jia, Nikhil KumarTomar, Debesh Jha, M. Riegler, P. Halvorsen, Sophia Bano, Uddhav Vaghela, Jianyuan Hong, Haili Ye, F. Huang, Da-han Wang, D. Stoyanov
/ 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.
Journal: ArXiv