--- language: en license: cc-by-sa-4.0 library_name: torch tags: - medical - segmentation - sam - medical-imaging - ct - mri - ultrasound pipeline_tag: image-segmentation datasets: - medical --- # MedSAM2: Segment Anything in 3D Medical Images and Videos

## Authors

Jun Ma^{* 1,2}, Zongxin Yang^{* 3}, Sumin Kim^2,4,5, Bihui Chen^2,4,5, Mohammed Baharoon^2,3,5,
Adibvafa Fallahpour^2,4,5, Reza Asakereh^4,7, Hongwei Lyu⁴, Bo Wang^{† 1,2,4,5,6}

^* Equal contribution ^† Corresponding author

¹AI Collaborative Centre, University Health Network, Toronto, Canada
²Vector Institute for Artificial Intelligence, Toronto, Canada
³Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA
⁴Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
⁵Department of Computer Science, University of Toronto, Toronto, Canada
⁶Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
⁷Roche Canada and Genentech

## Highlights - A promptable foundation model for 3D medical image and video segmentation - Trained on 455,000+ 3D image-mask pairs and 76,000+ annotated video frames - Versatile segmentation capability across diverse organs and pathologies - Extensive user studies in large-scale lesion and video datasets demonstrate that MedSAM2 substantially facilitates annotation workflows ## Model Overview 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. ## Available Models - **MedSAM2_2411.pt**: Base model trained in November 2024 - **MedSAM2_US_Heart.pt**: Fine-tuned model specialized for heart ultrasound video segmentation - **MedSAM2_MRI_LiverLesion.pt**: Fine-tuned model for liver lesion segmentation in MRI scans - **MedSAM2_CTLesion.pt**: Fine-tuned model for general lesion segmentation in CT scans - **MedSAM2_latest.pt** (recommended): Latest version trained on the combination of public datasets and newly annotated medical imaging data ## Downloading Models ### Option 1: Download individual models You can download the models directly from the Hugging Face repository: ```python # Using huggingface_hub from huggingface_hub import hf_hub_download # Download the recommended latest model model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_latest.pt") # Or download a specific fine-tuned model heart_us_model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_US_Heart.pt") liver_model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_MRI_LiverLesion.pt") ``` ### Option 2: Download all models to a specific folder ```python from huggingface_hub import hf_hub_download import os # Create checkpoints directory if it doesn't exist os.makedirs("checkpoints", exist_ok=True) # List of model filenames model_files = [ "MedSAM2_2411.pt", "MedSAM2_US_Heart.pt", "MedSAM2_MRI_LiverLesion.pt", "MedSAM2_CTLesion.pt", "MedSAM2_latest.pt" ] # Download all models for model_file in model_files: local_path = os.path.join("checkpoints", model_file) hf_hub_download( repo_id="wanglab/MedSAM2", filename=model_file, local_dir="checkpoints", local_dir_use_symlinks=False ) print(f"Downloaded {model_file} to {local_path}") ``` Alternatively, you can manually download the models from the [Hugging Face repository page](https://huggingface.co/wanglab/MedSAM2). ## Citations ``` @article{MedSAM2, title={MedSAM2: Segment Anything in 3D Medical Images and Videos}, author={Ma, Jun and Yang, Zongxin and Kim, Sumin and Chen, Bihui and Baharoon, Mohammed and Fallahpour, Adibvafa and Asakereh, Reza and Lyu, Hongwei and Wang, Bo}, journal={arXiv preprint arXiv:2504.03600}, year={2025} } ``` ## License The model weights can only be used for research and education purposes.