PedsBrainAutoSeg tool

This tool can be used to generate AI-predicted brain tumor segmentations for pediatric patients with multi-parametric MRIs. It was trained using the nnU-Net framework on a multi-institutional, heterogeneous dataset (see reference).

Based on 4 input image sequences per patient, the model will output a single prediction file with up to 4 tumor subregions:

1. Enhancing tumor
2. Non-enhancing tumor
3. Cyst
4. Edema

If you use this tool in your work, please cite the following reference accordingly:

1. Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Karthik Viswanathan, Wenxin Tu, Debanjan Haldar, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Phillip B. Storm, Adam Resnick, Jeffrey B. Ware, Ali Nabavizadeh, Anahita Fathi Kazerooni, "Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors", https://arxiv.org/abs/2401.08404

STEP 1: Prepare the input files

The model requires 4 images per subject (T2w-FLAIR, T1w, T1w post-contrast, T2w).

Preprocessing

Input files must be pre-processed, we recommend using the BraTS pipeline to follow the same pre-processing steps as was performed on the training data.

Organization

Pre-processed input files must be located in an input/ directory folder (called "input") and named with the following format: [subID]_[imageID]...[.nii/.nii.gz] where the imageID for each image type is:

Image type	imageID
T2w-FLAIR	FL
T1w	T1
T1w post-contrast	T1CE
T2w	T2

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
    ...

STEP 2: Usage

1. Install Docker
2. copy the docker-compose.yml file from this repository into the directory that contains your input/ folder:

   docker-compose.yml
   input/
       sub001_FL.nii.gz
       sub001_T1.nii.gz
       ...
   

3. from within that folder, run the command:

   docker compose up
   

It takes about an hour to fully process a single subject's data (depending on your machine specs). Model predictions will be stored in an output/ folder with files named [subID]_pred_brainTumorSeg.nii.gz .

Usage & Citations

Note: Use of this software is available to academic and non-profit institutions for research purposes only subject to the terms of the 2-Clause BSD License (see License). For use or transfers of the software to commercial entities, please inquire with Dr. Anahita Fathi Kazerooni - [email protected].

If you use the model in your research study, please cite the following paper(s):

1. Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Karthik Viswanathan, Wenxin Tu, Debanjan Haldar, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Phillip B. Storm, Adam Resnick, Jeffrey B. Ware, Ali Nabavizadeh, Anahita Fathi Kazerooni, "Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors", https://arxiv.org/abs/2401.08404