https://github.com/abysslover/deepparcellation
DeepParcellation: deep learning for robust brain MRI parcellation of elderly East Asians
https://github.com/abysslover/deepparcellation
brain mri parcellation-model
Last synced: about 1 year ago
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DeepParcellation: deep learning for robust brain MRI parcellation of elderly East Asians
- Host: GitHub
- URL: https://github.com/abysslover/deepparcellation
- Owner: abysslover
- License: gpl-3.0
- Created: 2021-07-20T01:19:58.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-12-12T05:16:35.000Z (over 3 years ago)
- Last Synced: 2025-04-13T10:12:06.678Z (about 1 year ago)
- Topics: brain, mri, parcellation-model
- Language: Python
- Homepage:
- Size: 98.6 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepParcellation Package
DeepParcellation: deep learning for robust brain MRI parcellation of elderly East Asians
### 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 Dementia (GARD) Cohort Research Center ([GARD CRC](http://nrcd.re.kr/)), Chosun University.
- This research was conducted in collaborations with the following people:
Eun-Cheon Lim1, Uk-Su Choi1, Kyu Yeong Choi1, Jang Jae Lee1, Yul-Wan Sung2, Seji Ogawa2, Byeong-Chae Kim3 Kun Ho Lee1 and Jungsoo Gim1.
1. Gwangju Alzheimer's & Related Dementia (GARD) Cohort Research Center, Chosun University, Gwangju, 61452, Republic of Korea
2. Kansei Fukushi Research Institute, Tohoku Fukushi University, Sendai, Miyagi, 9893201, Japan
3. Department of Neurology, Chonnam National University Medical School, Gwangju, 61469, Republic of Korea
- The manuscript is available in the [Frontiers in Aging Neuroscience](https://www.frontiersin.org/articles/10.3389/fnagi.2022.1027857/full).
## 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 python=3.8 -y
```
3. Install DeepParcellation (CPU mode)
```
conda activate deepparc
pip install deepparcellation
```
4. Install DeepParcellation (GPU mode)
```
conda activate deepparc
pip install deepparcellation
conda install cudnn=7.6.5 -c anaconda -y
conda install cudatoolkit=10.1.243 -c conda-forge -y
pip uninstall tensorflow -y
pip install tensorflow-gpu==2.2.0
pip uninstall keras -y
conda install keras-gpu=2.4.3 -c anaconda -y
```
5. Install DeepParcellation (Mac with M1 Chip)
```
conda activate deepparc
export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
export GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1
export CFLAGS="-I/opt/homebrew/opt/openssl/include"
export LDFLAGS="-L/opt/homebrew/opt/openssl/lib"
conda install -c apple tensorflow-deps -y
pip install tensorflow-macos
pip install deepparcellation
```
6. 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.