NiChart: Neuro Imaging Chart

NiChart is a comprehensive framework designed to revolutionize neuroimaging research. It offers large-scale neuroimaging capabilities, sophisticated analysis methods, and user-friendly tools, all integrated seamlessly into the AWS Cloud.

Components

1. Image Processing: Utilizes tools like DLMUSE, fMRIPrep XCEPengine, and QSIPrep for effective image analytics.
2. Reference Data Curation: Houses ISTAGING, 70000 Scans, and 14 individual studies to provide curated reference data.
3. Data Harmonization: Employs neuroharmonize and Combat for ensuring consistent data standards.
4. Machine Learning Models: Provides Supervised, Semi-supervised, and DL Models for advanced neuroimaging analysis.
5. Data Visualization: Features like Centile curves, direct image linking, and reference values for comprehensive data visualization.
6. Deployment: AWS Cloud App support with open-source Github components and Docker container compatibility.

Quick Links

Installation Instructions

1. git clone https://github.com/CBICA/NiChart_Project.git
2. git submodule update --init --recursive --remote
3. pip install -r requirements.txt

Example Usage

1. python3 run.py --dir_input input folder --dir_output output_folder --studies 1 --version my_version --cores 4 --conda 0 this will run the pipeline using 4 cores without initializing a new conda environment with an input folder containing 1 study
2. python3 run.py --dir_input input folder --dir_output output_folder --studies 2 --version my_version --cores 2 --conda 1 this will run the pipeline using 2 cores initializing a new conda environment with an input folder containing 2 studies
3. python3 run.py --dir_input input folder --dir_output output_folder --studies 2 --version my_version --cores 2 --conda 1 --dry_run 1 this will perform a dry run for the same parameters

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