# CaDIS: Cataract Dataset for Image Segmentation

arXiv: https://arxiv.org/abs/1906.11586 · HTML: https://mlsanity.com/paper/1906.11586/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [Towards Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2212.04582/) (2022)
- [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://mlsanity.com/paper/2211.16200/) (2022)
- [OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding](https://mlsanity.com/paper/2406.07471/) (2024)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets](https://mlsanity.com/paper/2204.05235/) (2022)
- [CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection](https://mlsanity.com/paper/2302.06294/) (2023)
- [Dynamic Scene Graph Representation for Surgical Video](https://mlsanity.com/paper/2309.14538/) (2023)
- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation](https://mlsanity.com/paper/2203.13409/) (2022)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [2020 CATARACTS Semantic Segmentation Challenge](https://mlsanity.com/paper/2110.10965/) (2021)
- [CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery](https://mlsanity.com/paper/2506.21813/) (2025)
- [MTCD: Cataract Detection via Near Infrared Eye Images](https://mlsanity.com/paper/2110.02564/) (2021)
- [RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation](https://mlsanity.com/paper/2504.07117/) (2025)
- [SURGIVID: Annotation-Efficient Surgical Video Object Discovery](https://mlsanity.com/paper/2409.07801/) (2024)
- [DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.05352/) (2021)
- [GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation](https://mlsanity.com/paper/2603.01108/) (2026)

## Abstract (arXiv)

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/.