Source code (Pytorch) for the paper: “Cross-Dimensional Knowledge-Guided Synthesizer Trained With Unpaired Multimodality MRIs”

Contact: 202103150302@zjut.edu.cn (Binjia Zhou) and zhouqianweischolar@gmail.com (Qianwei Zhou)

Configure the Enviroment

Depending on speed of your Internet connection，installation may take hours.

In our implemenation, we used python 3.8.18, Ubuntu 18.04.6 LTS with GPU.

1. nvidia gpu driver version: 530.30.02
2. cuda version: 12.1
3. GPU memory >= 12GB
4. install miniconda
5. $ conda create --name testENV --file requirements.txt -c pytorch
6. $ pip install pypng

You can train/test the CKG-GAN models by following instructions below.

1. BraTs2018: https://www.med.upenn.edu/sbia/brats2018/data.html
2. BraTs2021: http://braintumorsegmentation.org/
3. IXI dataset: https://brain-development.org/ixi-dataset/

Prepare Imaging Data

1. Adjust original images to the resolutions reported in the paper.
2. Place traing data to folder /datasets/,
   1. In /datasets/BraTs2018/, list all images of BraTs2018.
   2. In /datasets/BraTs2021/, list all images of BraTs2021.
   3. In /datasets/IXI/, list all images of IXI dataset.

Train CKG-GAN

You can obtain the pre-trained segmentation model and the student model for link https://pan.baidu.com/s/1EFgyt2YjGULHPEkhw37BQw?pwd=e2eb, the password is e2eb, then put them in the folder ./model.

$ python brats_4type_train.py to train image generator.

• Output:
  • The generator and discriminator models will be in the folder /outputs/brats_4type_train/checkpoints/.
  • They may look like dis_00040000.pt (the model of discriminator), gen_00040000.pt (the model of generator).

Test the CKG-GAN on Real Different Type images

• In the file brats_4type_test.py, please set pre-train model of your own that you are going to test.
• Please copy the target models (for example, gen_00040000.pt) to folder /outputs/brats_4type_train/checkpoints/.

$ python brats_4type_test.py

• Output: the code will generate target type images from input images.
  • Generated fake images will be in the folder ./test
  • For example: /Samples/realImages/3917L-CC-neg.png ---> ./test/output_num0001.jpg

The way of using the code for the IXI dataset can refer to the code logic of the aforementioned BraTs series of datasets.

The Work is Licensed with Apache License Version 2.0.