Installation¶

You will find below the steps for installing clinicadl on Linux or Mac. Please do not hesitate to contact us on the forum or GitHub if you encounter any issues.

Quick start¶

Python environment¶

You will need a Python environment to run Clinica. We advise you to use Miniconda. Miniconda allows you to install, run, and update Python packages and their dependencies. It can also create environments to isolate your libraries. To install Miniconda, open a new terminal and type the following commands:

If you are on Linux:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda-installer.sh
bash /tmp/miniconda-installer.sh

If you are on Mac:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o /tmp/miniconda-installer.sh
bash /tmp/miniconda-installer.sh

Installation of clinicadl¶

We recommend to use conda or virtualenv to install clinicadl inside a python environment. E.g.,

conda create --name clinicadl_env python=3.7
conda activate clinicadl_env
pip install clinicadl

You can also install the developer version from the repository: the active conda environment:

conda create --name clinicadl_env python=3.7
conda activate clinicadl_env
git clone git@github.com:aramis-lab/AD-DL.git
cd AD-DL
cd clinicadl
pip install -e .

Running the clinicadl environment¶

Activation of the clinicadl environment¶

Now that you have created the clinicadl environment, you can activate it:

conda activate clinicadl_env

Success

Congratulations, you have installed clinicadl! At this point, you can try the basic clinicadl -h command and get the help screen: ```Text (ClinicaDL)$ clinicadl -h usage: clinicadl [-h] [--verbose] {generate,preprocessing,extract,train,classify,tsvtool} ...

Clinica Deep Learning.

optional arguments: -h, --help show this help message and exit --verbose, -v

Task to execute with clinicadl:: What kind of task do you want to use with clinicadl? (tsvtool, preprocessing, extract, generate, train, validate, classify).

{generate,preprocessing,extract,quality_check,train,classify,tsvtool} ** Tasks proposed by clinicadl **** generate Generate synthetic data for functional tests. preprocessing Prepare data for training (needs clinica installed). extract Create data (slices or patches) for training. quality_check Performs quality check procedure for t1-linear pipeline.Original code can be found at https://github.com/vfonov/deep-qc train Train with your data and create a model. classify Classify one image or a list of images with your previously trained model. tsvtool Handle tsv files for metadata processing and data splits

```

Deactivation of the environment¶

At the end of your session, remember to deactivate your Conda environment:

conda deactivate