metadata
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: Medical Segment Anything Model v2
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, 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:
# 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
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.
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.