mlsanityData to 7 Oct 2026

2020 CATARACTS Semantic Segmentation Challenge

arXiv 2110.10965 ↗First posted 21 Oct 2021eess.IV cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 21 Oct 2021.

Builds on

21 of 28 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 3.

4 papers.

  1. 2023CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detectioncited by 22
  2. 2025The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessmentcited by 6
  3. 2025GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentationcited by 1
  4. 2025Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentationcited by 0