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.

通过在Segment Anything Model（SAM）中引入医学知识，我们介绍了SA-Med2D-20M，这是一个基于众多公共和私有数据集构建的大规模分割数据集，包含了460万个2D医学图像和1970万个相应的蒙版，并详细描述了该数据集的构建和处理方法。我们希望利用SA-Med2D-20M数据集的大规模和多样性来开发医学人工智能，以增强诊断、医学图像分析、知识共享和教育。

SA-Med2D-20M 数据集: 用 2000 万个掩模对 2D 医学影像进行分割