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.

Prepare your Python environment

You will need a Python environment to run ClinicaDL. 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
  

Install ClinicaDL

The latest release of ClinicaDL can be installed using pip as follows:

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

Run the ClinicaDL environment

Activation of the ClinicaDL environment

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

conda activate clinicadlEnv

Success

Congratulations, you have installed ClinicaDL! At this point, you can try the basic clinicadl -h command and get the help screen:

(clinicadlEnv)$ clinicadl -h
usage: clinicadl [-h] [--verbose]
                 {generate,preprocessing,extract,train,predict,tsvtool} ...

Deep learning software for neuroimaging datasets

optional arguments:
  -h, --help            show this help message and exit
  -l file.log, --logname file.log
                        Define the log file name (default: clinicaDL.log)
  -V, --version         ClinicaDL's installed version

Task to execute with clinicadl:
  What kind of task do you want to use with clinicadl?

  {generate,preprocessing,random-search,train,predict,tsvtool,interpret}
                        ****** Tasks proposed by clinicadl ******
    generate            Generate synthetic data for functional tests.
    preprocessing       Preprocess T1w-weighted images with t1-linear or
                        t1-extensive pipelines.
    random-search       Generate random networks to explore hyper parameters
                        space.
    train               Train with your data and create a model.
    predict             Performs the individual predictions of a list of
                        subject in tsv_path. If labels are given, will also
                        compute global metrics on the data set.
    tsvtool             Handle tsv files for metadata processing and data
                        splits.
    interpret           Interpret the prediction of a CNN with saliency maps.

Deactivation of the ClinicaDL environment

At the end of your session, you can deactivate your Conda environment:

conda deactivate