## Requirements
- tensorflow 1.13.1
- numpy 1.15.2
- opencv 4.1.0.25
- matplotlib 2.2.3
- pickleshare 0.7.4
- simpleitk 1.2.0
- scipy 1.1.0
## MR-Based Synthetic CT Generation Results
## Requirements
- tensorflow 1.13.1
- numpy 1.15.2
- opencv 4.1.0.25
- matplotlib 2.2.3
- pickleshare 0.7.4
- simpleitk 1.2.0
- scipy 1.1.0
## MR-Based Synthetic CT Generation Results
## Implementations
- Six cross-validations
- Data preprocessing methods: N4 bias correction, histogram matching, and mask generation
- U-Net for CNN model
- Encoder of the U-Net is initialized by pretrained VGG16 weights
- Best model is saved based on the MAE evaluation in validation data
- Tensorboard visualization
- MAE, ME, MSE, and PCC metrics in test data
## Documentation
### Dataset
Download our toy dataset from [here](https://www.dropbox.com/s/5wnd441uenbt5hp/brain01.zip?dl=0). This toy dataset just includes 367 paired images. We randomly divide data into training, validation, and test.
### Directory Hierarchy
```
.
│ MRI-to-CT-DCNN-TensorFlow
│ ├── src
│ │ ├── dataset.py
│ │ ├── get_mask.py
│ │ ├── histogram_matching.py
│ │ ├── main.py
│ │ ├── model.py
│ │ ├── n4itk.py
│ │ ├── preprocessing.py
│ │ ├── solver.py
│ │ └── utils.py
│ Data
│ └── brain01
│ │ └── raw
│ Models_zoo
│ └── caffe_layers_value.pickle
```
Download the pretrained VGG16 weights from [here](https://yunpan.360.cn/surl_yxLDnu6QqjQ) (password:3ouy).
### Data Preprocessing
Use `preprocessing.py` to rectify N4 bias correction, histogram matching, and head mask generation. Example usage:
```
python preprocessing.py --delay=1 --is_save
```
- `data`: dataset path for preprocessing, default: `../../Data/brain01/raw`
- `temp_id`: template image id for histogram matching, default: `2`
- `size`: 'image width and height (width == height), default: `256`
- `delay`: interval time when showing image, default: `1`
- `is_save`: save processed image or not, default: `False`
- N4 bias field correction using [N4ITK](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5445030)
- Histogram matching using [Dynamic histogram warping algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=537491)
- Binary head mask using [Otsu auto-thresholding](https://pdfs.semanticscholar.org/fa29/610048ae3f0ec13810979d0f27ad6971bdbf.pdf)
### Training DCNN
Use `main.py` to train a DCNN model. Example usage:
```
python main.py --is_train
```
- `gpu_index`: gpu index if you have multiple gpus, default: `0`
- `is_train`: training or test mode, default: `False (test mode)`
- `batch_size`: batch size for one iteration, default: `8`
- `dataset`: dataset name, default: `brain01`
- `learning_rate`: learning rate, default: `2e-4`
- `epoch`: number of epochs, default: `600`
- `print_freq`: print frequency for loss information, default: `100`
- `load_model`: folder of saved model that you wish to continue training, (e.g. 20190411-2217), default: `None`
### Test DCNN
Use `main.py` to test the DCNN model. Example usage:
```
python main.py --load_model=folder/you/wish/to/test/e.g./20190411-2217
```
please refer to the above arguments.
### Tensorboard Visualization
Evaluation of the MAE, ME, MSE, and PCC in validation data during training process. Different color represents different model in six cross-validations.
Total loss, data loss and regularization term in each iteration.
### Test Evaluation
MAE, ME, MSE, and PCC for six models and average performance.
### Citation
```
@misc{chengbinjin2019DCNN,
author = {Cheng-Bin Jin},
title = {MRI-to-CT-DCNN-Tensorflow},
year = {2019},
howpublished = {\url{https://github.com/ChengBinJin/MRI-to-CT-DCNN-TensorFlow},
note = {commit xxxxxxx}
}
```
## References
- Han, Xiao. "MR‐based synthetic CT generation using a deep convolutional neural network method." Medical physics 44.4 (2017): 1408-1419.
- Tustison, Nicholas J., et al. "N4ITK: improved N3 bias correction." IEEE transactions on medical imaging 29.6 (2010): 1310.
- Cox, Ingemar J., Sébastien Roy, and Sunita L. Hingorani. "Dynamic histogram warping of image pairs for constant image brightness." Proceedings., International Conference on Image Processing. Vol. 2. IEEE, 1995.
- Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics 9.1 (1979): 62-66.
## License
Copyright (c) 2018 Cheng-Bin Jin. Contact me for commercial use (or rather any use that is not academic research) (email: sbkim0407@gmail.com). Free for research use, as long as proper attribution is given and this copyright notice is retained.