Installation

Prerequisites

SCT runs on Linux and OSX; native Windows support is not there but Docker might be used to run a Linux container on Windows.

For Linux, the prerequisites should be already available on mainstream distributions. SCT has been tested on Debian >= 7, Fedora >= 23, Ubuntu >= 14.04, Gentoo et al.

For OSX, there are no prerequisites.

Installation using git

You may have good reasons to want to install a development version of SCT.

SCT will still bundle its own Python distribution, installed with all the required packages, and using specific package versions, in order to ensure reproducibility of results.

Procedure:

  1. Retrieve the SCT code

    Clone the repository and hop inside:

    git clone https://github.com/neuropoly/spinalcordtoolbox

cd spinalcordtoolbox

  2. Checkout the revision of interest, if different from master:

    git checkout ${revision_of_interest}

  3. Run the installer and follow the instructions

    ./install_sct

Docker

Docker is a portable (Linux, OSX, Windows) container platform.

In the context of SCT, it can be used:

  • To run SCT on Windows, until SCT can run natively there
  • For development testing of SCT, faster than running a full-fledged virtual machine
  • <your reason here>

See https://github.com/neuropoly/sct_docker for more information.

Hard-core Installation-less SCT usage

This is completely unsupported.

Procedure:

  1. Retrieve the SCT code

  2. Install dependencies

    Example for Ubuntu 18.04:

    # The less obscure ones may be packaged in the distribution
sudo apt install python3-{numpy,scipy,nibabel,matplotlib,h5py,mpi4py,keras,tqdm,sympy,requests,sklearn,skimage}
# The more obscure ones would be on pip
sudo apt install libmpich-dev
pip3 install --user distribute2mpi nipy dipy

    Example for Debian 8 Jessie:

    # The less obscure ones may be packaged in the distribution
sudo apt install python3-{numpy,scipy,matplotlib,h5py,mpi4py,requests}
# The more obscure ones would be on pip
sudo apt install libmpich-dev
pip3 install --user distribute2mpi sympy tqdm Keras nibabel nipy dipy scikit-image sklearn

  3. Prepare the runtime environment

    # Create launcher-less scripts
mkdir -p bin
find scripts/ -executable | while read file; do ln -sf "../${file}" "bin/$(basename ${file//.py/})"; done
PATH+=":$PWD/bin"

# Download binary programs
mkdir bins
pushd bins
sct_download_data -d binaries_debian
popd
PATH+=":$PWD/bins"

# Download models & cie
mkdir data; pushd data; for x in PAM50 gm_model optic_models pmj_models deepseg_sc_models deepseg_gm_models ; do sct_download_data -d $x; done; popd

# Add path to spinalcordtoolbox to PYTHONPATH
export PYTHONPATH="$PWD:$PWD/scripts"