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NiChart_Tissue_Segmentation

Brain tissue segmentation using FSL FAST and DLICV

Overview

NiChart_Tissue_Segmentation offers easy brain tissue segmantation.

This is achieved through the DLICV and FAST methods.

Given an input (sMRI) scan, NiChart_Tissue_Segmentation extracts the following:

  1. Tissue segmentation
  2. Volumetric data (optional)

This package uses nnUNet (version 1) as a basis model architecture for the deep learning parts, FAST for the tissue segmentation and various other libraries.

It is available both as an installable package, as well as a docker container. Please see Installation and Usage for more information on how to use it.

Installation

  1. Create a new conda env

    conda create --name NCTS python=3.8
conda activate NCTS

    In one command:

    conda create --name NCTS -y python=3.8 && conda activate NCTS

  2. Clone and install NiChart_Tissue_Segmentation

    git clone  https://github.com/CBICA/NiChart_Tissue_Segmentation.git
cd NiChart_Tissue_Segmentation
pip install .

  3. Download model from this package's release as an artifact

  4. Run NiChart_Tissue_Segmentation. Please see Usage for an example.

Docker-based build

The package comes already pre-built as a docker container, for convenience. Please see Usage for more information on how to use it. Alternatively, you can build the docker image locally using the dockerfile provided, like so:

docker build -t nichart_tissue_segmentation .

Usage

Pre-trained nnUNet models for the skull-stripping task can be found in the NiChart_Tissue_Segmentation - 0.0.0 release as an artifact. Feel free to use it under the package's license.

The model provided as an artifact is already in the file structure that's needed for the package to work, so make sure to include it as downloaded.

Given the following file structure:

temp
├── in                      // Input folder. Image names are irrelevant.
│   ├── image1.nii.gz
│   ├── image2.nii.gz
│   └── image3.nii.gz
├── nnUNet_model            // As provided from the release
│   └── nnUNet
└── out                     // Output destination
    ├── image1_seg.nii.gz
    ├── image2_seg.nii.gz
    ├── image3_seg.nii.gz
    └── output.csv

As a locally installed package

A complete command would be (run from the directory of the package):

NiChart_Tissue_Segmentation -i /temp/in/ \
                            -o /temp/out/ \
                            -m /temp/nnUNet_model \
                            -c /temp/output.csv # Optional

For further explanation please refer to the complete documentation:

NiChart_Tissue_Segmentation -h

Using the docker container

An example command using the docker container is the following:

docker run -it --rm --gpus all -v ./:/workspace/ aidinisg/nichart_tissue_segmentation:0.0.0 NiChart_Tissue_Segmentation -i path/to/input -o path/to/output

Please note that the model is provided in the docker container, but you can always substitute it with your own nnUNet model.

Example output

temp
├── in              
│   ├── image1.nii.gz
│   ├── image2.nii.gz
│   └── image3.nii.gz
├── nnUNet_model    
│   └── nnUNet
└── out                     // Output destination
    ├── image1_seg.nii.gz
    ├── image2_seg.nii.gz
    ├── image3_seg.nii.gz
    └── output.csv

Contact

For more information, please contact CBICA Software.

For Developers

Contributions are welcome! Please refer to our CONTRIBUTING.md for more information on how to report bugs, suggest enhancements, and contribute code.

If you're a developer looking to contribute, you'll first need to set up a development environment. After cloning the repository, you can install the development dependencies with:

pip install -r requirements-test.txt

This will install the packages required for running tests and formatting code. Please make sure to write tests for new code and run them before submitting a pull request.

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