# CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

arXiv: https://arxiv.org/abs/2012.12453 · HTML: https://mlsanity.com/paper/2012.12453/

## Key facts

- Cited by 106 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 30 of 106)

- [Advancing Surgical VQA with Scene Graph Knowledge](https://mlsanity.com/paper/2312.10251/) (2023)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation](https://mlsanity.com/paper/2308.03726/) (2023)
- [Follow Anything: Open-set detection, tracking, and following in real-time](https://mlsanity.com/paper/2308.05737/) (2023)
- [Text Promptable Surgical Instrument Segmentation with Vision-Language Models](https://mlsanity.com/paper/2306.09244/) (2023)
- [General surgery vision transformer: A video pre-trained foundation model for general surgery](https://mlsanity.com/paper/2403.05949/) (2024)
- [Scaling up self-supervised learning for improved surgical foundation models](https://mlsanity.com/paper/2501.09436/) (2025)
- [Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation](https://mlsanity.com/paper/2601.10880/) (2026)
- [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)
- [The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark](https://mlsanity.com/paper/2312.12429/) (2023)
- [LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings](https://mlsanity.com/paper/2503.19740/) (2025)
- [Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art](https://mlsanity.com/paper/2304.13014/) (2023)
- [A spatio-temporal network for video semantic segmentation in surgical videos](https://mlsanity.com/paper/2306.11052/) (2023)
- [CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery](https://mlsanity.com/paper/2406.16039/) (2024)
- [General-purpose foundation models for increased autonomy in robot-assisted surgery](https://mlsanity.com/paper/2401.00678/) (2024)
- [Metrics Matter in Surgical Phase Recognition](https://mlsanity.com/paper/2305.13961/) (2023)
- [Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning](https://mlsanity.com/paper/2312.06829/) (2023)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [Surgical-LLaVA: Toward Surgical Scenario Understanding via Large Language and Vision Models](https://mlsanity.com/paper/2410.09750/) (2024)
- [S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.06447/) (2024)
- [Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applications](https://mlsanity.com/paper/2309.03048/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [Instrument-tissue Interaction Detection Framework for Surgical Video Understanding](https://mlsanity.com/paper/2404.00322/) (2024)
- [Interactive Generation of Laparoscopic Videos with Diffusion Models](https://mlsanity.com/paper/2406.06537/) (2024)
- [Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models](https://mlsanity.com/paper/2410.07753/) (2024)
- [HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2506.21287/) (2025)
- [Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization](https://mlsanity.com/paper/2311.02719/) (2023)
- [Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation](https://mlsanity.com/paper/2312.03043/) (2023)
- [Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything](https://mlsanity.com/paper/2403.08003/) (2024)
- [Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer Vision](https://mlsanity.com/paper/2407.17904/) (2024)

## Abstract (arXiv)

Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) is limited. This article elaborates on the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [3], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in Cholec80 and annotated the images. The dataset is named CholecSeg8K and its total size is 3GB. Each of these images is annotated at pixel-level for thirteen classes, which are commonly founded in laparoscopic cholecystectomy surgery. CholecSeg8k is released under the license CC BY- NC-SA 4.0.