caGAN-SIM

caGAN-SIM software is a tensorflow implementation for deep learning-based 3D-SIM reconstruction. This repository is developed based on the 2021 IEEE JSTQE paper 3D Structured Illumination Microscopy via Channel Attention Generative Adversarial Network.

Author: Chang Qiao^1,#, Xingye Chen^1,#, Siwei Zhang^2,#, Di Li^2,3, Yuting Guo^2,3, Qionghai Dai^1,+, Dong Li^2,3,4,+
¹Department of Automation, Tsinghua University, Beijing, China.
²National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
³College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
⁴Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.
^#Equal contribution.
⁺Correspondence to: qhdai@tsinghua.edu.cn and lidong@ibp.ac.cn

Environment
File structure
Test pre-trained models
Train a new model
License
Citation

Environment

Ubuntu 16.04
CUDA 11.0.207
cudnn 8.0.4
Python 3.6.10
Tensorflow 2.4.0
GPU: GeForce RTX 2080Ti

File structure

./dataset is the default path for training data and testing data
- ./dataset/train The augmented training image patch pairs should be saved here by default
- ./dataset/test includes some demo images of F-actin and microtubules to test caGAN-SIM models
./src includes the source codes of caGAN-SIM
- ./src/models includes declaration of caGAN models
- ./src/utils is the tool package of caGAN-SIM software
./trained_models place pre-trained caGAN-SIM models here for testing, and newly trained models will be saved here by default

Test pre-trained models

Place your testing data in ./dataset/test
Open your terminal and cd to ./src
Run bash demo_predict.sh in your terminal. Note that before running the bash file, you should check if the data paths and other arguments in demo_predict.sh are set correctly
The output reconstructed SR images will be saved in --data_dir

Train a new model

Data for training: You can train a new caGAN-SIM model using your own datasets. Note that you'd better divide the dataset of each specimen into training part and validation/testing part before training, so that you can test your model with the preserved validation/testing data
Run bash demo_train.sh in your terminal to train a new caGAN-SIM model. Similar to testing, before running the bash file, you should check if the data paths and the arguments are set correctly
You can run tensorboard --logdir [save_weights_dir]/[save_weights_name]/graph to monitor the training process via tensorboard. If the validation loss isn't likely to decay any more, you can use early stop strategy to end the training
Model weights will be saved in ./trained_models/ by default

License

This repository is released under the MIT License (refer to the LICENSE file for details).

Citation

If you find the code helpful in your resarch, please cite the following paper:

@article{qiao20213d,
  title={3D Structured Illumination Microscopy via Channel Attention Generative Adversarial Network},
  author={Qiao, Chang and Chen, Xingye and Zhang, Siwei and Li, Di and Guo, Yuting and Dai, Qionghai and Li, Dong},
  journal={IEEE Journal of Selected Topics in Quantum Electronics},
  volume={27},
  number={4},
  pages={1--11},
  year={2021},
  publisher={IEEE}
}

qc17-THU/caGAN-SIM