Metadata-Version: 2.1
Name: DLICV
Version: 0.0.0
Summary: DLICV - Deep Learning Intra Cranial Volume
Home-page: https://github.com/georgeaidinis/DLICV/
Download-URL: https://github.com/georgeaidinis/DLICV/
Author: Ashish Singh, Guray Erus, Vishnu Bashyam, George Aidinis
Author-email: software@cbica.upenn.edu
Maintainer: George Aidinis
Maintainer-email: aidinisg@pennmedicine.upenn.edu
License: By installing/using DeepMRSeg, 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.
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: nnunet >=1.7.0
Requires-Dist: torch <2.1,>2.0
Provides-Extra: test
Requires-Dist: nnunet >=1.7.0 ; extra == 'test'
Requires-Dist: torch <2.1,>2.0 ; extra == 'test'
Requires-Dist: pytest >=7.0.0 ; extra == 'test'

# DLICV - Deep Learning Intra Cranial Volume

## Overview

DLICV uses a trained [nnUNet](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) 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/georgeaidinis/DLICV
cd DLICV
conda create -n DLICV -y python=3.8 && conda activate DLICV
pip install .
```

## Usage

A pre-trained nnUNet model can be found in the [DLICV-0.0.0 release](https://github.com/georgeaidinis/DLICV/releases/tag/v0.0.0) as an [artifact](https://github.com/georgeaidinis/DLICV/releases/download/v0.0.0/model.zip). Feel free to use it under the package's [license](LICENSE).

### Import as a python package

```python
from dlicv.compute_icv import compute_volume

# Assuming your nifti file is named 'input.nii.gz'
volume_image = compute_volume("input.nii.gz", "output.nii.gz", "path/to/model/")
```

### From the terminal

```bash
DLICV --input input.nii.gz --output output.nii.gz --model path/to/model
```

Replace the `input.nii.gz` with the path to your input nifti file, as well as the model path.

Example:

Assuming a file structure like so:

```bash
.
├── in
│   ├── input1.nii.gz
│   ├── input2.nii.gz
│   └── input3.nii.gz
├── model
│   ├── fold_0
│   ├── fold_1
│   │   ├── debug.json
│   │   ├── model_final_checkpoint.model
│   │   ├── model_final_checkpoint.model.pkl
│   │   ├── model_latest.model
│   │   ├── model_latest.model.pkl
│   └── plans.pkl
└── out
```

An example command might be:

```bash
DLICV --input path/to/input/ --output path/to/output/ --model path/to/model/
```

### Using the docker container

In the docker container, the.

## Contact

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