# SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

arXiv: https://arxiv.org/abs/2311.11969 · HTML: https://mlsanity.com/paper/2311.11969/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 143 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 30 of 68)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [SegGPT: Segmenting Everything In Context](https://mlsanity.com/paper/2304.03284/) (2023)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)

## Built on by (most cited first, 30 of 55)

- [VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks](https://mlsanity.com/paper/2406.08394/) (2024)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI](https://mlsanity.com/paper/2408.03361/) (2024)
- [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine](https://mlsanity.com/paper/2412.09278/) (2024)
- [Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2410.18387/) (2024)
- [GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI](https://mlsanity.com/paper/2411.14522/) (2024)
- [Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE](https://mlsanity.com/paper/2409.17508/) (2024)
- [A Refer-and-Ground Multimodal Large Language Model for Biomedicine](https://mlsanity.com/paper/2406.18146/) (2024)
- [Towards a vision foundation model for comprehensive assessment of Cardiac MRI](https://mlsanity.com/paper/2410.01665/) (2024)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images](https://mlsanity.com/paper/2502.14351/) (2025)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?](https://mlsanity.com/paper/2505.24173/) (2025)
- [MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision](https://mlsanity.com/paper/2508.08177/) (2025)
- [Ophiuchus: Incentivizing Tool-augmented "Think with Images" for Joint Medical Segmentation, Understanding and Reasoning](https://mlsanity.com/paper/2512.14157/) (2025)
- [MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs](https://mlsanity.com/paper/2510.01691/) (2025)
- [MediSee: Reasoning-based Pixel-level Perception in Medical Images](https://mlsanity.com/paper/2504.11008/) (2025)
- [PRS-Med: Position Reasoning Segmentation in Medical Imaging](https://mlsanity.com/paper/2505.11872/) (2025)
- [Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations](https://mlsanity.com/paper/2501.02385/) (2025)
- [SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues](https://mlsanity.com/paper/2406.19364/) (2024)
- [Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts](https://mlsanity.com/paper/2409.06821/) (2024)
- [MedicoSAM: Robust Improvement of SAM for Medical Imaging](https://mlsanity.com/paper/2501.11734/) (2025)
- [RadSAM: Segmenting 3D radiological images with a 2D promptable model](https://mlsanity.com/paper/2504.20837/) (2025)
- [When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models](https://mlsanity.com/paper/2603.23085/) (2026)
- [OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine](https://mlsanity.com/paper/2402.18028/) (2024)
- [SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging](https://mlsanity.com/paper/2403.16578/) (2024)

## Abstract (arXiv)

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D.