Metadata-Version: 2.1
Name: DLMUSE
Version: 1.0.0
Summary: DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters
Home-page: https://github.com/CBICA/DLMUSE/
Download-URL: https://github.com/CBICA/DLMUSE/
Author: Ashish Singh, Guray Erus, Vishnu Bashyam, George Aidinis
Author-email: software@cbica.upenn.edu
Maintainer: George Aidinis, Spiros Maggioros, Kyunglok Baik
Maintainer-email: aidinisg@pennmedicine.upenn.edu, Spiros.Maggioros@pennmedicine.upenn.edu, kyunglok.baik@pennmedicine.upenn.edu
License: By installing/using DLMUSE, the user agrees to the following license: See https://www.med.upenn.edu/cbica/software-agreement-non-commercial.html
Keywords: deep learning,image segmentation,semantic segmentation,medical image analysis,medical image segmentation,nnU-Net,nnunet
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch==2.2.1
Requires-Dist: nnunetv2==2.5.1
Requires-Dist: argparse
Requires-Dist: huggingface-hub
Requires-Dist: pathlib

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

## Overview

DLMUSE uses a trained [nnUNet](https://github.com/MIC-DKFZ/nnUNet) model to compute the segmentation of the brain into [MUSE](https://www.med.upenn.edu/cbica/sbia/muse.html) ROIs from the nifti image of the Intra Cranial Volume (ICV - see [DLICV method](https://github.com/CBICA/DLICV)), 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

```bash
pip install DLMUSE
```

## Directly from this repository

```bash
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](https://huggingface.co/nichart/DLMUSE/tree/main) or at the [DLMUSEV2-1.0.0 release](https://github.com/CBICA/DLMUSE/releases/tag/v1.0.0). Feel free to use it under the package's [license](LICENSE).

### From command line
```bash
DLMUSE -i "image_folder" -o "path to output folder" -device cuda/mps/cpu
```

## Contact

For more information, please contact [CBICA Software](mailto:software@cbica.upenn.edu).

## For Developers

Contributions are welcome! Please refer to our [CONTRIBUTING.md](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:

```bash
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.
