DLMUSE 1.0.0

DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters

DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters

Overview

DLMUSE uses a trained nnUNet model to compute the segmentation of the brain into MUSE ROIs from the nifti image of the Intra Cranial Volume (ICV - see DLICV method), oriented in LPS orientation. It produces the segmented brain, along with a .csv file of the calculated volumes of each ROI.

Installation

As a python package

pip install DLMUSE

Directly from this repository

git clone https://github.com/CBICA/DLMUSE
cd DLMUSE
pip install -e .

Usage

A pre-trained nnUNet model can be found at our hugging face account or at the DLMUSEV2-1.0.0 release. Feel free to use it under the package's license.

From command line

DLMUSE -i "image_folder" -o "path to output folder" -device cuda/mps/cpu

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.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.