Installation

Scikit-rt has a number of dependencies. To avoid possible version conflicts, it’s recommended that it be installed to a clean virtual environment. This is achieved in the instructions below using conda. You can perform a user installation with pip, or a developer installation with git. Installations have been tested for Python 3.8 and Python 3.10.

For functionality involving image registration, it is also necessary to install image-registration software.

Installation and setup of scikit-rt

To be able to use scikit-rt, you can perform either a user installation (recommended if you’re not planning to contribute to the code base) or a developer installation.

  1. User installation

conda create --name skrt python=3.10
conda activate skrt
pip install scikit-rt

  1. Developer installation

git clone https://github.com/scikit-rt/scikit-rt
cd scikit-rt
conda env create --file environment.yml
conda activate skrt

  1. Environment activation and deactivation

Following either of the installations above, the scikit-rt environment setup is included - that is, the conda skrt environment is active. When starting a new session, the environment can be activated and deactivated with:

# Activate envrionment
conda activate skrt

# Deactivate environment
conda deactivate

  1. Installation test

As a minimal test that the installation has been successful, try:

python -c "import skrt; print(skrt.__version__)"

This may take some time, while python code is being compiled, but should eventually print the scikit-rt version number, and exit without errors.

Updating scikit-rt

Scikit-rt is in active development. Following an initial installation, it’s possible to update to the latest version.

  1. User installation

With the conda skrt environment active, run the command:

pip install --upgrade scikit-rt

  1. Developer installation

From the scikit-rt directory, and assuming that no unmerged changes have been made to the local copy of the code, run the command:

git pull

Installation and setup for image registration

For image registration, and for atlas-based segmentation, scikit-rt requires that at least one of the following image-registration packages be installed:

There are three options for setting up the environment to allow use by scikit-rt of the registration software (registration engine):

  1. Before starting scikit-rt, follow the instructions linked for the relevant registration package.

  2. At run time, use code similar to the following :

    from skrt.registration import set_engine_dir

set_engine_dir("/path/to/elastix/directory", engine="elastix")
set_engine_dir("/path/to/niftyreg/directory", engine="niftyreg")

 # matlab-skrt option 1: use source code with MATLAB executable.
 Defaults().matlab_app = "/path/to/directory/containing/MATLAB/executable"
 set_engine_dir("/path/to/directory/containing/mskrt/package"

 # matlab-skrt option 2: use compiled code with MATLAB runtime environment.
 Defaults().matlab_runtime = "/path/to/matlab/runtime/install/directory"
 set_engine_dir("/path/to/directory/containing/matlabreg/executable", engine="matlab")

    The parameter engine passed to the set_engine_dir() function may be omitted if the name of the registration engine is a substring of the installation path.

  3. When creating registration or segmentation objects, pass the installation directory of the registration engine to be used via the engine_dir parameter. When using matlab-skrt, the location of the MATLAB executable or of the MATLAB runtime installation must still be set:

    from skrt.core import Defaults

# matlab-skrt option 1: define location of MATLAB executable.
Defaults().matlab_app = "/path/to/directory/containing/MATLAB/executable"

# matlab-skrt option 2: define location of MATLAB runtime installation.
Defaults().matlab_runtime = "/path/to/matlab/runtime/install/directory"