This is the official PyTorch implementation of FSM (Fourier Style Mining) (MIA 2022).

Source Free Domain Adaptation for Medical Image Segmentation with Fourier Style Mining[Paper]

Chen Yang, Xiaoqing Guo, Zhen Chen, Yixuan Yuan

Get Started

Environment

Install dependencies

pip install -r requirements.txt

Datasets Preparation

EndoScene and ETIS-Larib

(1) Download the EndoScene and ETIS-Larib dataset.

(2) Put the data in the corresponding folders. The dataset files are organized as follows.

SFDA-FSM
├── data
│   ├── EndoScene
│   │   ├── images
│   │   │   ├── [case_id].png
│   │   ├── labels
│   │   │   ├── [case_id].png
│   ├── ETIS-Larib
│   │   ├── images
│   │   │   ├── [case_id].png
│   │   ├── labels
│   │   │   ├── [case_id].png

(3) Split dataset into training set and test set as follows.

python preprocess.py

Training

Generation Stage

(1) Generate source-like images with pretrained source model as follows.

python tools/domain_inversion.py 

(2) Visualization of source-like images.

Adaptation Stage

python tools/train_adapt.py 

Testing

python tools/test.py 

Citation

If you find this project useful, please consider citing:

@article{yang2022source,
  title={Source free domain adaptation for medical image segmentation with fourier style mining},
  author={Yang, Chen and Guo, Xiaoqing and Chen, Zhen and Yuan, Yixuan},
  journal={Medical Image Analysis},
  volume={79},
  pages={102457},
  year={2022},
  publisher={Elsevier}
}