Awesome
<h1 align="center">ā Medical SAM 2: Segment Medical Images As Video Via Segment Anything Model 2</h1> <p align="center"> <a href="https://discord.gg/DN4rvk95CC"> <img alt="Discord" src="https://img.shields.io/discord/1146610656779440188?logo=discord&style=flat&logoColor=white"/></a> <img src="https://img.shields.io/static/v1?label=license&message=GPL&color=white&style=flat" alt="License"/> </p>Medical SAM 2, or say MedSAM-2, is an advanced segmentation model that utilizes the SAM 2 framework to address both 2D and 3D medical image segmentation tasks. This method is elaborated on the paper Medical SAM 2: Segment Medical Images As Video Via Segment Anything Model 2 and Medical SAM 2 Webpage.
š„ A Quick Overview
<div align="center"><img width="880" height="350" src="https://github.com/MedicineToken/Medical-SAM2/blob/main/vis/framework.png"></div>š©» 3D Abdomen Segmentation Visualisation
<div align="center"><img width="420" height="420" src="https://github.com/MedicineToken/Medical-SAM2/blob/main/vis/example.gif"></div>š§ Requirement
Install the environment:
conda env create -f environment.yml
conda activate medsam2
You can download SAM2 checkpoint from checkpoints folder:
bash download_ckpts.sh
Further Note: We tested on the following system environment and you may have to handle some issue due to system difference.
Operating System: Ubuntu 22.04
Conda Version: 23.7.4
Python Version: 3.12.4
We released our pretrain weight here
šÆ Example Cases
Download REFUGE or BCTV or your own dataset and put in the data folder, create the folder if it does not exist āļø
2D case - REFUGE Optic-cup Segmentation from Fundus Images
Step1: Download pre-processed REFUGE dataset manually from here, or using command lines:
wget https://huggingface.co/datasets/jiayuanz3/REFUGE/resolve/main/REFUGE.zip
unzip REFUGE.zip
Step2: Run the training and validation by:
python train_2d.py -net sam2 -exp_name REFUGE_MedSAM2 -vis 1 -sam_ckpt ./checkpoints/sam2_hiera_small.pt -sam_config sam2_hiera_s -image_size 1024 -out_size 1024 -b 4 -val_freq 1 -dataset REFUGE -data_path ./data/REFUGE
3D case - Abdominal Multiple Organs Segmentation
Step1: Download pre-processed BTCV dataset manually from here, or using command lines:
wget https://huggingface.co/datasets/jiayuanz3/btcv/resolve/main/btcv.zip
unzip btcv.zip
Step2: Run the training and validation by:
python train_3d.py -net sam2 -exp_name BTCV_MedSAM2 -sam_ckpt ./checkpoints/sam2_hiera_small.pt -sam_config sam2_hiera_s -image_size 1024 -val_freq 1 -prompt bbox -prompt_freq 2 -dataset btcv -data_path ./data/btcv
šØ News
- 24-12-04. Our Medical SAM 2 paper was updated on Arxiv with new insights and results
- 24-08-05. Our Medical SAM 2 paper ranked #1 Paper of the day collected by AK on Hugging Face š¤
- 24-08-05. Update 3D example details and pre-processed BTCV dataset download link š
- 24-08-05. Update 2D example details and pre-processed REFUGE dataset download link š
- 24-08-05. Our Medical SAM 2 paper was available online š„³
- 24-08-05. Our Medical SAM 2 code was available on Github š„³
- 24-07-30. The SAM 2 model was released š¤©
š Cite
@misc{zhu2024medical,
title={Medical SAM 2: Segment medical images as video via Segment Anything Model 2},
author={Jiayuan Zhu and Abdullah Hamdi and Yunli Qi and Yueming Jin and Junde Wu},
year={2024},
eprint={2408.00874},
archivePrefix={arXiv},
primaryClass={cs.CV}
}