Metadata-Version: 2.1
Name: deepparcellation
Version: 1.0.29
Summary: DeepParcellation: 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
Description: # 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>, Kun-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. Kansei Fukushi Research Institute, Tohoku Fukushi 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 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 (Force)
        ```
        	conda activate deepparc
        	pip install --force --no-dependencies 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**.
        2. You should install DeepParcellation with force parameter when all the other dependencies were manually met, i.e., you have installed tensorflow-macos, tensorflow-metal dependencies for Apple M1 chips.
        
        ### Contact
        Please contact abysslover@gmail.com if you have any questions about DeepParcellation.
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
