169-samus-adapting-segment-anything-model-for-clinically-friendly-and-generalizable-ultrasound-imag

https://github.com/szu-advtech-2023/169-samus-adapting-segment-anything-model-for-clinically-friendly-and-generalizable-ultrasound-imag

Repository

https://github.com/SZU-AdvTech-2023/169-SAMUS-Adapting-Segment-Anything-Model-for-Clinically-Friendly-and-Generalizable-Ultrasound-Imag/blob/main/

# SAMUS
This repo is the official implementation for:\
[SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation.](https://arxiv.org/pdf/2309.06824.pdf)\
(The details of our SAMUS can be found in the models directory in this repo or in the paper.)

## Highlights
 Low GPU requirements. (one 3090ti with 24G GPU memory is enough)\
 Large ultrasound dataset. (about 30K images and 69K masks covering 6 categories)\
 Excellent performance, especially in generalization ability.

## Installation
Following [Segment Anything](https://github.com/facebookresearch/segment-anything), `python=3.8.16`, `pytorch=1.8.0`, and `torchvision=0.9.0` are used in SAMUS.

1. Clone the repository.
    ```
    git clone https://github.com/xianlin7/SAMUS.git
    cd SAMUS
    ```
2. Create a virtual environment for SAMUS and activate the environment.
    ```
    conda create -n SAMUS python=3.8
    conda activate SAMUS
    ```
3. Install Pytorch and TorchVision.
   (you can follow the instructions [here](https://pytorch.org/get-started/locally/))
5. Install other dependencies.
  ```
    pip install -r requirements.txt
  ```
## Checkpoints
We use checkpoint of SAM in [`vit_b`](https://github.com/facebookresearch/segment-anything) version.

## Data
- US30K consists of seven publicly-available datasets, including [TN3K]( https://github.com/haifangong/TRFE-Net-for-thyroid-nodule-segmentation), [DDTI]( https://github.com/haifangong/TRFE-Net-for-thyroid-nodule-segmentation), [TG3K](https://github.com/haifangong/TRFE-Net-for-thyroid-nodule-segmentation), [BUSI](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset), [UDIAT](http://www2.docm.mmu.ac.uk/STAFF/M.Yap/dataset.php), [CAMUS](http://camus.creatis.insa-lyon.fr/challenge/), and [HMC-QU](https://aistudio.baidu.com/aistudio/datasetdetail/102406).
- All images were saved in PNG format. No special pre-processed methods are used in data preparation.
- We have provided some examples to help you organize your data. Please refer to the file fold [example_of_required_dataset_format](https://github.com/xianlin7/SAMUS/tree/main/example_of_required_dataset_format).\
  Specifically, each line in train/val.txt should be formatted as follows:
  ```
    //
  ```
- The relevant information of your data should be set in [./utils/config.py](https://github.com/xianlin7/SAMUS/blob/main/utils/config.py) 

## Training
Once you have the data ready, you can start training the model.
```
cd "/home/...  .../SAMUS/"
python train.py --modelname SAMUS --task 
```
## Testing
Do not forget to set the load_path in [./utils/config.py](https://github.com/xianlin7/SAMUS/blob/main/utils/config.py) before testing.
```
python test.py --modelname SAMUS --task 
```

## Citation
If our SAMUS is helpful to you, please consider citing:
```
@misc{lin2023samus,
      title={SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation}, 
      author={Xian Lin and Yangyang Xiang and Li Zhang and Xin Yang and Zengqiang Yan and Li Yu},
      year={2023},
      eprint={2309.06824},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

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