Metadata-Version: 2.1
Name: deepparcellation
Version: 1.0.1
Summary: DeepParcellation: fast and accurate fast and accurate brain MRI parcellation by deep learning
Home-page: https://github.com/abysslover/deepparcellation
Author: Eun-Cheon Lim
Author-email: abysslover@gmail.com
License: GPL3.0
Keywords: brain MRI parcellation tensorflow keras
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# DeepParcellation Package
DeepParcellation: fast and accurate brain MRI parcellation by deep learning



### Contributions
- The project was initiated by Dr. Lim (abysslover) and Dr. Choi (yooha1003).
- The code is written by Dr. Lim at Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center ([GARD CRC](http://nrcd.re.kr/)), Chosun University.
- This research was conducted in collaborations with the following people:
Eun-Cheon Lim<sup>1</sup>, Uk-Su Choi<sup>1</sup>, Yul-Wan Sung<sup>2</sup>, Gun-Ho Lee<sup>1</sup> and Jungsoo Gim<sup>1</sup>.

1. Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, Chosun University, Gwangju, Republic of Korea
2. Department of Brain Imaging, Tohoku University, Sendai, Miyagi, Japan

- The manuscript will be available in the future.

## Getting Started
A step-by-step tutorial is provided in the following sections.

### Prerequisites
You should install CUDA-enabled GPU cards with at least 8GB GPU memory manufactured by nVidia, e.g., Titan XP.

### Prepare T1-weighted MR images

1. Convert MR images to Neuroimaging Informatics Technology Initiative (NIfTI) format.
2. The parent directory name of a NIfTI file path will be used as **Subject Id** during prediction.
3. You can specify either **an input path** of the NIfTI file or **input direcotry** of many NIfTI files.

### Install DeepParcellation
1. Install Anaconda
   - Download an Anaconda distribution: [Link](https://www.anaconda.com/distribution/)
2. Create a Conda environment
```
	conda create -n deepparc
```
3. Install DeepParcellation in Linux
```
	conda activate deepparc
	pip install deepparcellation
```

4. To run DeepParcellation using GPU
```
	conda install cudnn=7.6.5 -c anaconda -y
	conda install cudatoolkit=10.1.243 -c conda-forge -y
	pip unnstall keras -y
	conda install keras-gpu=2.4.3 -c anaconda -y
```
5. Run DeepParcellation
```
	conda activate deepparc
	deepparcellation -o=/tmp/test --i=./subject-0-0000/test.nii.gz
	or
	deepparcellation -o=/tmp/test --i=./
```
**NOTE**:
1. You must always **activate the conda enviroment** before running DeepParcellation if you opened a **new console**.

### Contact
Please contact abysslover@gmail.com if you have any questions about DeepParcellation.

