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--- |
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datasets: |
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- medical |
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language: en |
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library_name: torch |
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license: cc-by-sa-4.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- medical |
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- segmentation |
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- sam |
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- medical-imaging |
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- ct |
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- mri |
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- ultrasound |
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--- |
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# MedSAM2: Segment Anything in 3D Medical Images and Videos |
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<div align="center"> |
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<table align="center"> |
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<tr> |
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<td><a href="https://arxiv.org/abs/2504.03600" target="_blank"><img src="https://img.shields.io/badge/arXiv-Paper-FF6B6B?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper"></a></td> |
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<td><a href="https://github.com/bowang-lab/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="Code"></a></td> |
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<td><a href="https://huggingface.co/wanglab/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/HuggingFace-Model-FFBF00?style=for-the-badge&logo=huggingface&logoColor=white" alt="HuggingFace Model"></a></td> |
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</tr> |
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<tr> |
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<td><a href="https://medsam-datasetlist.github.io/" target="_blank"><img src="https://img.shields.io/badge/Dataset-List-00B89E?style=for-the-badge" alt="Dataset List"></a></td> |
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<td><a href="https://huggingface.co/datasets/wanglab/CT_DeepLesion-MedSAM2" target="_blank"><img src="https://img.shields.io/badge/Dataset-CT__DeepLesion-28A745?style=for-the-badge" alt="CT_DeepLesion-MedSAM2"></a></td> |
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<td><a href="https://huggingface.co/datasets/wanglab/LLD-MMRI-MedSAM2" target="_blank"><img src="https://img.shields.io/badge/Dataset-LLD--MMRI-FF6B6B?style=for-the-badge" alt="LLD-MMRI-MedSAM2"></a></td> |
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</tr> |
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<tr> |
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<td><a href="https://github.com/bowang-lab/MedSAMSlicer/tree/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/3D_Slicer-Plugin-000000?style=for-the-badge" alt="3D Slicer"></a></td> |
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<td><a href="https://github.com/bowang-lab/MedSAM2/blob/main/app.py" target="_blank"><img src="https://img.shields.io/badge/Gradio-Demo-F9D371?style=for-the-badge&logo=gradio&logoColor=white" alt="Gradio App"></a></td> |
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<td><a href="https://colab.research.google.com/drive/1MKna9Sg9c78LNcrVyG58cQQmaePZq2k2?usp=sharing" target="_blank"><img src="https://img.shields.io/badge/Colab-Notebook-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white" alt="Colab"></a></td> |
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</tr> |
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</table> |
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</div> |
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[Project Page](https://medsam2.github.io/) |
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## Authors |
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<p align="center"> |
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<a href="https://scholar.google.com.hk/citations?hl=en&user=bW1UV4IAAAAJ&view_op=list_works&sortby=pubdate">Jun Ma</a><sup>* 1,2</sup>, |
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<a href="https://scholar.google.com/citations?user=8IE0CfwAAAAJ&hl=en">Zongxin Yang</a><sup>* 3</sup>, |
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Sumin Kim<sup>2,4,5</sup>, |
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Bihui Chen<sup>2,4,5</sup>, |
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<a href="https://scholar.google.com.hk/citations?user=U-LgNOwAAAAJ&hl=en&oi=sra">Mohammed Baharoon</a><sup>2,3,5</sup>,<br> |
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<a href="https://scholar.google.com.hk/citations?user=4qvKTooAAAAJ&hl=en&oi=sra">Adibvafa Fallahpour</a><sup>2,4,5</sup>, |
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<a href="https://scholar.google.com.hk/citations?user=UlTJ-pAAAAAJ&hl=en&oi=sra">Reza Asakereh</a><sup>4,7</sup>, |
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Hongwei Lyu<sup>4</sup>, |
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<a href="https://wanglab.ai/index.html">Bo Wang</a><sup>† 1,2,4,5,6</sup> |
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</p> |
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<p align="center"> |
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<sup>*</sup> Equal contribution <sup>†</sup> Corresponding author |
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</p> |
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<p align="center"> |
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<sup>1</sup>AI Collaborative Centre, University Health Network, Toronto, Canada<br> |
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<sup>2</sup>Vector Institute for Artificial Intelligence, Toronto, Canada<br> |
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<sup>3</sup>Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA<br> |
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<sup>4</sup>Peter Munk Cardiac Centre, University Health Network, Toronto, Canada<br> |
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<sup>5</sup>Department of Computer Science, University of Toronto, Toronto, Canada<br> |
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<sup>6</sup>Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada<br> |
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<sup>7</sup>Roche Canada and Genentech |
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</p> |
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## Highlights |
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- A promptable foundation model for 3D medical image and video segmentation |
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- Trained on 455,000+ 3D image-mask pairs and 76,000+ annotated video frames |
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- Versatile segmentation capability across diverse organs and pathologies |
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- Extensive user studies in large-scale lesion and video datasets demonstrate that MedSAM2 substantially facilitates annotation workflows |
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## Model Overview |
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MedSAM2 is a promptable segmentation segmentation model tailored for medical imaging applications. Built upon the foundation of the [Segment Anything Model (SAM) 2.1](https://github.com/facebookresearch/sam2), MedSAM2 has been specifically adapted and fine-tuned for various 3D medical images and videos. |
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