# 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).
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