ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

This is the official implementation of the paper " ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising" in 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The pre-print version can be found in arxiv; camera-ready version will be soon released.

Updates

Sep, 2023: initial commit.
Dec, 2023: update data proprocessing file: /data/data_preprocessing.ipynb.

Approach

Data Preparation

The 2016 AAPM-Mayo dataset can be downloaded from: CT Clinical Innovation Center (B30 kernel)
The 2020 AAPM-Mayo dataset can be downloaded from: cancer imaging archive

Dataset structre:

Mayo2016_2d/
  |--train/
      |--quarter_1mm/
        train_quarter_00001.npy
        train_quarter_00002.npy
        train_quarter_00003.npy
        ...
      |--full_1mm/
        train_full_00001.npy
        train_full_00002.npy
        train_full_00003.npy
        ...
  |--test/
      |--quarter_1mm
      |--full_1mm

Requirements

- Linux Platform
- torch==1.12.1+cu113 # depends on the CUDA version of your machine
- torchvision==0.13.1+cu113
- Python==3.8.0
- numpy==1.22.3

Traning and & Inference

Training

python train.py  --name ASCON(experiment_name)   --model ASCON  --netG  ESAU  --dataroot /data/zhchen/Mayo2016_2d(path to images) --nce_layers  1,4 --layer_weight 1,1  --num_patches 32,512  --k_size 3,7 --lr 0.0002 --gpu_ids 6,7 --print_freq 25 --batch_size 8 --lr_policy cosine

Inference & testing

python test.py  --name ASCON(experiment_name)   --model ASCON  --netG ESAU --results_dir test_results --result_name ASCON_results(path to save image)   --gpu_ids 6 --batch_size 1 --eval

Please refer to options files for more setting.

Citation

If you find our work and code helpful, please kindly cite the corresponding paper:

@article{chen2023ascon,
  title={ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising},
  author={Chen, Zhihao and Gao, Qi and Zhang, Yi and Shan, Hongming},
  journal={MCCAI 2023},
  year={2023}
}