● Medical SAM 2: Segment Medical Images As Video Via Segment Anything Model 2
Medical SAM 2, or say MedSAM-2, is an advanced segmentation model that utilizes the [SAM 2](https://github.com/facebookresearch/segment-anything-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](https://arxiv.org/abs/2408.00874) and [Medical SAM 2 Webpage](https://supermedintel.github.io/Medical-SAM2/).
## 🔥 A Quick Overview
## 🩻 3D Abdomen Segmentation Visualisation
## Pre-trained weight
We released our pretrain weight [here](https://huggingface.co/jiayuanz3/MedSAM2_pretrain/tree/main)
## 🧐 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
```
## 🎯 Example Cases
#### Download REFUGE or BCTV or your own dataset and put in the ``data`` folder, create the folder if it does not exist ⚒️
### 2D case - REFUGE Optic-cup Segmentation from Fundus Images
**Step1:** Download pre-processed [REFUGE](https://refuge.grand-challenge.org/) dataset manually from [here](https://huggingface.co/datasets/jiayuanz3/REFUGE/tree/main), 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](https://www.synapse.org/#!Synapse:syn3193805/wiki/217752) dataset manually from [here](https://huggingface.co/datasets/jiayuanz3/btcv/tree/main), 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``
## 🚨 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 🤗
- 24-08-05. Update 3D example details and pre-processed BTCV dataset download link 🔗
- 24-08-05. Update 2D example details and pre-processed REFUGE dataset download link 🔗
- 24-08-05. Our Medical SAM 2 paper was available online 🥳
- 24-08-05. Our Medical SAM 2 code was available on Github 🥳
- 24-07-30. The SAM 2 model was released 🤩
## 📝 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}
}
~~~
