Segment anything model (SAM) has revolutionized natural image segmentation,
but its performance on medical images is limited. This work presents MedSAM,
the first attempt at extending the success of SAM to medical images, with the
goal of creating a universal tool for the segmentation of various medical
targets. Specifically, we first curate a large-scale medical image dataset,
encompassing over 200,000 masks across 11 different modalities. Then, we
develop a simple fine-tuning method to adapt SAM to general medical image
segmentation. Comprehensive experiments on 21 3D segmentation tasks and 9 2D
segmentation tasks demonstrate that MedSAM outperforms the default SAM model
with an average Dice Similarity Coefficient (DSC) of 22.5% and 17.6% on 3D and
2D segmentation tasks, respectively. The code and trained model are publicly
available at https://github.com/bowang-lab/MedSAM.

MedSAM 是第一次尝试将 SAM 的成功延伸到医学图像中，通过构建一个包含超过 200,000 个掩膜的大规模医学图像数据集，并开发了一种简单的微调方法，以适应普通医学图像分割，在 21 个 3D 分割任务和 9 个 2D 分割任务的全面实验中，展示了超过默认 SAM 模型的平均 Dice 相似系数 (DSC) 分别达到了 22.5% 和 17.6 %。