README.md

Preprocess MR-CT data and generate masks

• For simplicity, we assume the dataset have MRI and CT scans.
• This code assumes that you have the raw data in three folders: train, val and test.
• train set contains only unpaired images, whereas val and test set contain paired images.

Environment installation

Setup using pip install -r requirements.txt

Data preparation

• Refer to your root folder as root. Assume your data structure is as follows

├── root/
   ├── train/
   |   ├── MRI/
   │   │   ├── filename001.nii
   │   │   ├── filename002.nii
   │   │   └── ...
   |   └── CT/
   |       ├── filename001.nii
   |       ├── filename002.nii
   |       └── ...   
   ├── val/
   |   ├── MRI/
   |   |   ├── filename003.nii
   |   |   └── filename004.nii
   |   └── CT/
   |       ├── filename003.nii
   |       └── filename004.nii
   └── test/
       ├── MRI/
       |   ├── filename005.nii
       |   └── filename006.nii
       └── CT/
           ├── filename005.nii
           └── filename006.nii

Preprocess

3 arguments that you can specify:

• --root: data root;
• --out: output directory name, default is processed-mr-ct;
• --resample: resample the resolution of the medical scans, default is [1.0, 1.0, 1.0] mm^3.

Since our paediatric scans have irregular sizes, we need to crop the depth and height dimensions in function crop_scan() at Ln 47. When running, the preprocessed 2D slice visualizations are saved under vis for your inspection. Use them to modify data augmentation crop_scan() as needed.

Update!

Now, to use the based position selection strategy, our preprocessing stage generate files as: filename_XXX.jpg, where XXX corresponds to the relative position of the slice respect to the entire volumetric image. In that way, our model can choose slices of similar position.