SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

• URL: http://arxiv.org/abs/2310.15161v3
• Date: Sat, 14 Sep 2024 05:30:41 GMT
• Title: SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
• Authors: Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, Yu Qiao,
• Abstract summary: We introduce SAM-Med3D for general-purpose segmentation on volumetric medical images. SAM-Med3D can accurately segment diverse anatomical structures and lesions across various modalities. Our approach demonstrates that substantial medical resources can be utilized to develop a general-purpose medical AI.
• Score: 35.83393121891959
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumetric medical images. Given only a few 3D prompt points, SAM-Med3D can accurately segment diverse anatomical structures and lesions across various modalities. To achieve this, we gather and process a large-scale 3D medical image dataset, SA-Med3D-140K, from a blend of public sources and licensed private datasets. This dataset includes 22K 3D images and 143K corresponding 3D masks. Then SAM-Med3D, a promptable segmentation model characterized by the fully learnable 3D structure, is trained on this dataset using a two-stage procedure and exhibits impressive performance on both seen and unseen segmentation targets. We comprehensively evaluate SAM-Med3D on 16 datasets covering diverse medical scenarios, including different anatomical structures, modalities, targets, and zero-shot transferability to new/unseen tasks. The evaluation shows the efficiency and efficacy of SAM-Med3D, as well as its promising application to diverse downstream tasks as a pre-trained model. Our approach demonstrates that substantial medical resources can be utilized to develop a general-purpose medical AI for various potential applications. Our dataset, code, and models are available at https://github.com/uni-medical/SAM-Med3D.

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