# Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection

arXiv: https://arxiv.org/abs/2312.06295 · HTML: https://mlsanity.com/paper/2312.06295/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks](https://mlsanity.com/paper/1808.08127/) (2018)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery](https://mlsanity.com/paper/1806.05796/) (2018)
- [Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization](https://mlsanity.com/paper/2104.14280/) (2021)
- [CataNet: Predicting remaining cataract surgery duration](https://mlsanity.com/paper/2106.11048/) (2021)
- [LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos](https://mlsanity.com/paper/2107.00875/) (2021)
- [Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation](https://mlsanity.com/paper/1903.11834/) (2019)
- [Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training](https://mlsanity.com/paper/2307.16660/) (2023)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception](https://mlsanity.com/paper/2312.03409/) (2023)
- [Action Recognition in Video Recordings from Gynecologic Laparoscopy](https://mlsanity.com/paper/2311.18666/) (2023)
- [Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning](https://mlsanity.com/paper/2312.03401/) (2023)

## Built on by (most cited first, 8 of 8)

- [OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding](https://mlsanity.com/paper/2406.07471/) (2024)
- [Segment anything model 2: an application to 2D and 3D medical images](https://mlsanity.com/paper/2408.00756/) (2024)
- [SASVi -- Segment Any Surgical Video](https://mlsanity.com/paper/2502.09653/) (2025)
- [Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2505.07691/) (2025)
- [SG2VID: Scene Graphs Enable Fine-Grained Control for Video Synthesis](https://mlsanity.com/paper/2506.03082/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation](https://mlsanity.com/paper/2509.10748/) (2025)
- [SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation](https://mlsanity.com/paper/2605.16530/) (2026)

## Abstract (arXiv)

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons' skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. Particularly, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations will be publicly available upon acceptance of the paper.