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Step 1: unpaired image translation with dynamic network

Our synthesis network is used for 3D unpaired image translation, with controllable output styles by conditioning on a one-hot code.

method

Data preparation

CrossMoDA 2023 dataset can be officially downloaded here.

Once the dataset is downloaded, we perform the following preprocessing steps:

  • Resample to the median resolution of the dataset, i.e., 0.41 x 0.41 x 1 mm3
  • Based on the cochlea locations, crop the images to 256 x 144 x 32 sub-volumes.

preprocess

Once the preprocessing is done, we need to arrange the data in the following format. Note that A and B correspond to the source and target domain, respectively. The filenames in the ImagesA and LabelsA should be the same. EdgesA stores the weighting maps used for re-weighting the edge loss. If EdgesA is not provided, the edge loss will simply treat each voxel equally.

Data/
|-----ImagesA
|-----ImagesB
|-----LabelsA
|-----EdgesA (optional)

For source domain, ImagesA and LabelsA, the filenames should be arranged as follows.

ImagesA/                                         LabelsA/
|-----crossmoda2023_etz_1.nii.gz                 |-----crossmoda2023_etz_1.nii.gz
|-----crossmoda2023_etz_2.nii.gz                 |-----crossmoda2023_etz_2.nii.gz
|-----...                                        |-----...
|-----crossmoda2023_ldn_1.nii.gz                 |-----crossmoda2023_ldn_1.nii.gz
|-----crossmoda2023_ldn_2.nii.gz                 |-----crossmoda2023_ldn_2.nii.gz
|-----...                                        |-----...
|-----crossmoda2023_ukm_1.nii.gz                 |-----crossmoda2023_ukm_1.nii.gz
|-----crossmoda2023_ukm_2.nii.gz                 |-----crossmoda2023_ukm_2.nii.gz
|-----...                                        |-----...

For target domain, ImagesB, the filename should include a tag that specifies the site information (or any other categorical information), which will be taken by the network as the condition to generate controllable styles. In our example, the tags are 'etz', 'ldn', and 'ukm'.

ImagesB/
|-----crossmoda2023_etz_106.nii.gz
|-----crossmoda2023_etz_107.nii.gz
|-----...
|-----crossmoda2023_ldn_80.nii.gz
|-----crossmoda2023_ldn_81.nii.gz
|-----...
|-----crossmoda2023_ukm_44.nii.gz
|-----crossmoda2023_ukm_45.nii.gz
|-----...

Create a virtual environment using Anaconda

Here we create a virtual environment called 'crossmoda2023' and install the dependencies.

conda create -n crossmoda2023 python=3.9 -y
conda activate crossmoda2023
pip install -r requirements.txt

Train

python train.py -n YourExperimentName -d /Data

During training, you can visualize your intermediate results at /checkpoints/YourExperimentName/web/index.html

playground

More training configurations can be found here.

Test

We use the input argument 'code' to control which site-specific style to generate. For the CrossMoDA 2023, we use 001, 010, 100 to represent three sub-folders prepared by the challenge organizers, i.e., UKM, ETZ, LDN, respectively.

python test.py -n YourExperimentName -i /Data/ImagesA --code 0 0 1

Playground

If you just want to play with our pre-trained model and have fun with the controllable styles, this is what you are looking for!

Step 1: download the sample data and the pre-trained model to your local machine

I have prepared several preprocessed data and our pre-trained model in this 'sample' folder. Once you finish downloading this folder, you need to move the 'pretrained' folder to the checkpoint directory.

Step 2: time to play!

You can either generate the known site-specific styles using the codes that the network has been trained with, i.e., (0, 0, 1), (0, 1, 0), (1, 0, 0), or generate some unknown T2 styles by using any codes you like (even some crazy ones, e.g., (1, 5, 10)).

You will find that different codes will only have impacts on the image style, not the anatomy! Disentanglement, huh?

python play_ground.py -n pretrained --src_image SourceImagePath --src_label SourceLabelPath --save_dir OutputFolder --code 1 2 3

Some examples of unknown styles are shown below:

playground