SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
This dataset solves the problem of limited labeled medical data for researchers building AI models in medical imaging, though it is incremental as it extends an existing model to a new domain.
The paper introduces SA-Med2D-20M, a large-scale dataset of 4.6 million 2D medical images and 19.7 million masks to address the poor performance of the Segment Anything Model (SAM) in medical image segmentation due to lack of medical training data, enabling the development of medical vision foundation models.
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.