aparida12/hail2024 repository overview

⁠Neural Network-Based Pediatric Brain MR-T1 Harmonization

This repository contains the inference engine for Pediatric Brain MR-T1 Harmonization. The inference engine is dockerized to allow a plug-and-play approach to harmonizing NifTi data.

⁠Easily harmonize your data with one command

Once all the docker and its dependencies are installed, you can test the pediatric brain harmonization by running :

Run with GPUs:

default:

docker run -v <inpdir>:/data_directory -v <outdir>:/output_directory/ aparida12/hail2024:20241031 --in-path /data_directory/<input name> --target-image /data_directory/<template name> --out-path /output_directory/<output name>

where:

• <inpdir> is the directory with all input MRI NifTi files(extn .nii.gz; .nii will be ignored) that need to be segmented.
• <outdir> is the directory where all outputs are stored(<inpdir> should not be <outdir>).

⁠Input Folder Structure

The input folder should follow a similar structure as shown below in <inpdir>. file_name can be anything.

│
└───<inpdir>
│   │   file_name.nii.gz
│    |.

⁠Citations

If you use and/or refer to this software in your research, please cite the following papers:

• Parida, Abhijeet, Zhifan Jiang, Syed Muhammad Anwar, Nicholas Foreman, Nicholas Stence, Michael J. Fisher, Roger J. Packer, Robert A. Avery, and Marius George Linguraru. "Harmonization Across Imaging Locations (HAIL): One-Shot Learning for Brain MRI." arXiv preprint arXiv:2308.11047 (2023).

⁠Disclaimer

This software is provided without any warranties or liabilities and is intended for research purposes only. It has not been reviewed or approved for clinical use by the Food and Drug Administration (FDA) or any other federal or state agency.

⁠External Links

• Code Repo⁠
• Interactive Webapp⁠