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Segment Anything Model (SAM) has achieved impressive results for naturalimage segmentation with input prompts such as points and bounding boxes. Itssuccess largely owes to massive labeled training data. However, directlyapplying SAM to medical image segmentation cannot perform well because SAMlacks medical knowledge -- it does not use medical images for training. Toincorporate medical knowledge into SAM, we introduce SA-Med2D-20M, alarge-scale segmentation dataset of 2D medical images built upon numerouspublic and private datasets. It consists of 4.6 million 2D medical images and19.7 million corresponding masks, covering almost the whole body and showingsignificant diversity. This paper describes all the datasets collected inSA-Med2D-20M and details how to process these datasets. Furthermore,comprehensive statistics of SA-Med2D-20M are presented to facilitate the betteruse of our dataset, which can help the researchers build medical visionfoundation models or apply their models to downstream medical applications. Wehope that the large scale and diversity of SA-Med2D-20M can be leveraged todevelop medical artificial intelligence for enhancing diagnosis, medical imageanalysis, knowledge sharing, and education. The data with the redistributionlicense is publicly available at https://github.com/OpenGVLab/SAM-Med2D.

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