Metadata-Version: 2.1
Name: DLICV
Version: 1.0.1
Summary: DLICV - Deep Learning Intra Cranial Volume.
Home-page: https://github.com/CBICA/DLICV/
Download-URL: https://github.com/CBICA/DLICV/
Author: Ashish Singh, Guray Erus, Vishnu Bashyam, George Aidinis, Alexander Getka
Author-email: software@cbica.upenn.edu
Maintainer: George Aidinis, Spiros Maggioros, Kyunglok Baik, Alexander Getka
Maintainer-email: aidinisg@pennmedicine.upenn.edu, Spiros.Maggioros@pennmedicine.upenn.edu, kyunglok.baik@pennmedicine.upenn.edu, alexander.getka@pennmedicine.upenn.edu
License: By installing/using DLICV, 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

# DLICV - Deep Learning Intra Cranial Volume

## Overview

DLICV uses a trained [nnUNet](https://github.com/MIC-DKFZ/nnUNet) model to compute the intracranial volume from structural MRI scans in the nifti image format, oriented in _**LPS**_ orientation.

## Installation

### As a python package
```bash
pip install DLICV
```
### Directly from this repository
```bash
git clone https://github.com/CBICA/DLICV
cd DLICV
pip install -e .
```

### Installing PyTorch
Depending on your system configuration and supported CUDA version, you may need to follow the [PyTorch Installation Instructions](https://pytorch.org/get-started/locally/). 

## Usage
A pre-trained nnUNet model can be found at our [hugging face account](https://huggingface.co/nichart/DLICV).
Feel free to use it under the package's [licence](LICENCE)
```bash
DLICV -i "input_folder" -o "output_folder" -device 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.
Please make sure to write tests for new code and run them before submitting a pull request.
