Script to visualize preprocessed images as per nnUNet plans.json

[rohanbanerjee]

Description:

When a dataset is given to nnUNet, it self-configures a few preprocessing parameters on it's own. These pre-processing steps are then applied to the images for the training. This script takes the parameters from the nnUNet plans.json file and applies it to the images so that we can visualize what the preprocessed images look like and take decisions of changing the parameters as per requirement.

[naga-karthik]

Thanks for creating the script! correct me if am wrong, but it seems that we input a nifti file and get a Z-score normalized patch as the output.

But this isn't the preprocessing that nnunet does right? It also does resampling and cropping to foreground.

Moreover, would it be better to find a way to visualize the patches after data augmentation? I mean, preprocessing is only resampling, foreground-cropping and normalization -- we all know how the images could like after this. I am thinking that the real question would be visualize what happens ot the patches after data aug