README.md

Pediatric automated single- or multi-parametric skull stripping with nnUNet

This pipeline can be used to generate AI-predicted brain masks and skull-stripped images for pediatric patients with single- or multi-parametric MRIs. It was trained using the nnU-Net framework on a multi-institutional, heterogeneous dataset.

Dependencies include:

1. Python 3.9
2. PyTorch
3. nnUNet v1

The package will run nnUNet testing/inference with the pre-trained auto-skull-stripping model on the input files.

Acknowledgement

Ariana Familiar, PhD, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia

STEP 1: Prepare the input files

Required inputs: All of the following pre-processed pediatric brain MRI scans for the multi-parametric model OR just one of the scans for the single-parametric model:

1. T1-weighted pre-contrast (T1w)
2. T1-weighted post-contrast (T1w post-contrast)
3. T2-weighted (T2w)
4. T2-weighted FLAIR (T2w-FLAIR)

Input files (raw data) must be located in an directory folder and named with the following format: [subID]_[imageID]...[.nii/.nii.gz] where the imageID for each image type is:

Image type	imageID	nnUNet naming
T2w-FLAIR	FL	0000
T1w	T1	0001
T1w post-contrast	T1CE	0002
T2w	T2	0003

NOTE: the exact file format is required with an underscore: [subID]_[imageID]

For example:

input/
    sub001_FL.nii.gz
    sub001_T1.nii.gz
    sub001_T1CE.nii.gz
    sub001_T2.nii.gz
    sub002_FL.nii.gz
    ...

Configured to run on CPU.

STEP 2: Usage

Install Docker

Copy all files into a single directory

1. copy the appropriate .yml file from this repository into the directory that contains your input/ folder: docker-compose_single-parametric.yml for Single-parametric input docker-compose_multi-parametric.yml for Multi-parametric inputs

   Single-parametric model example:

   docker-compose_single-parametric.yml
   input/
       sub001_FL.nii.gz
       sub002_T2.nii.gz
       ...
   

   Multi-parametric model example:

   docker-compose_multi-parametric.yml
   input/
       sub001_FL.nii.gz
       sub001_T1.nii.gz
       sub001_T1CE.nii.gz
       sub001_T2.nii.gz
       ...
   

2. from within that folder, run the command:

   docker compose -f docker-compose_single-parametric.yml up
   
   or
   
   docker compose -f docker-compose_multi-parametric.yml up
   

Available models:

• nnUNet-based skull-stripping using single-parametric brain MRI scans as input: Version 1
• nnUNet-based skull-stripping using multi-parametric brain MRI scans as input: Version 1

References

• Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z

Licenses

This software includes third party open source software components with their own licenses:

• nnUnet: Apache-2.0 license : https://github.com/MIC-DKFZ/nnUNet?tab=Apache-2.0-1-ov-file