[MICCAI2023] The official implementation of Low-dose CT image super-resolution network with dual-guidance feature distillation and dual-path content communication.
This repository is modified from BasicSR. Thanks for the open source code of BasicSR.
conda create -n new_env python=3.9.7 -y
conda activate new_env
pip install -r requirements.txt
pip install -e .More details could be found in the installation ducoment of BasicSR.
You should prepare your data in this way:
data_rootdir
- dataset_name
- img
- hr_nd
- train
- val
- test
- lr_ld
- x2
- train
- train_avg
- val
- val_avg
- test
- test_avg
- x4
- train
- train_avg
- val
- val_avg
- test
- test_avg
- lr_nd
- x2
- train
- val
- test
- x4
- train
- val
- test
-mask
- hr
- train
- val
- test
- x2
- train
- val
- test
- x4
- train
- val
- test
And you should modify the path in configuration files in "opations/train/*.yml" or "opations/test/*.yml".
Run:
python basicsr/train.py --opt options/train/your_config_file.ymlThe model files will be saved in "experiments" folder.
Firstly, you should modify the model paths in "opations/test/*.yml". Then, run:
python basicsr/test.py --opt options/test/your_config_file.ymlThe results will be saved in "results" folder.
An example, including models and dataset, could be found in BaiduDisk:z3gy.
@inproceedings{chi2023low,
title={Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication},
author={Chi, Jianning and Sun, Zhiyi and Zhao, Tianli and Wang, Huan and Yu, Xiaosheng and Wu, Chengdong},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={98--108},
year={2023},
organization={Springer}
}