2021/10/21 by Imanol Luengo, Maria Grammatikopoulou, Luengo, Imanol +76 · 1 citation
Engineering · Medicine · #Anatomy and Medical Technology #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging in Medicine #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Surgical Simulation and Training #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.10965
openalex publication_date 2021/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.