Warning

ivadomed is no more maintained. New models integrated in our 3rd party software (SCT, AxonDeepSeg, etc.) are now trained using MONAI and/or nnUnet.

ivadomed is an integrated framework for medical image analysis with deep learning.

The technical documentation is available here. The more detailed installation instruction is available there

Installation

ivadomed requires Python >= 3.7 and < 3.10 as well as PyTorch == 1.8. We recommend working under a virtual environment, which could be set as follows:

python -m venv ivadomed_env
source ivadomed_env/bin/activate

Install from release (recommended)

Install ivadomed and its requirements from Pypi <https://pypi.org/project/ivadomed/>__:

pip install --upgrade pip
pip install ivadomed

Install from source

Bleeding-edge developments builds are available on the project's master branch on Github. Installation procedure is the following:

git clone https://github.com/neuropoly/ivadomed.git
cd ivadomed
pip install -e .

Contributors

This project results from a collaboration between the NeuroPoly Lab and the Mila. The main funding source is IVADO.

List of contributors

Consult our Wiki(https://github.com/ivadomed/ivadomed/wiki) here for more help