The purpose of the ivadomed project is to:

  • Provide researchers with an open-source framework for training deep learning models for applications in medical imaging;
  • Provide ready-to-use Pre-trained models trained on multi-center data.

Comparison with other projects

We acknowledge the existence of projects with similar purposes. The table below compares some features across some of the existing projects. This table was mostly based on the existing documentation for each project. We understand that the field is rapidly evolving, and that this table might reflect the reality. If you notice inconsistencies, please let us know by opening an issue.

Feature ivadomed monai delira MIC-DKFZ ANTsPyNet DLTK MIScnn niftytorch DeepNeuro DeepReg
BIDS (1)

DL base library PyTorch PyTorch PyTorch, TF PyTorch TF/Keras TF TF/Keras PyTorch TF/Keras TF
Task (2) Class, Seg, Detect Seg, Class Class, Gen, Seg Detect Class, Seg, Clust, Reg Class, Seg, Reg Seg Class, Seg Seg Reg
Data dimension 2D, 3D 2D, 3D 2D, 3D 2D, 3D 2D, 3D 3D 2D, 3D 3D 2D, 3D 2D, 3D



Transfer Learning

Pre-processing tools

Post-processing tools

User case examples

Multi-GPU data parallelism

Model evaluation

Input region of interest

Missing modality

Models comparison

Hyperparam optimisation

Multi-center models

(1): “BIDS” stands for the Brain Imaging Data Structure, which is a convention initiated by the neuroimaging community to organize datasets (filenames, metadata, etc.). This facilitates the sharing of datasets and minimizes the burden of organizing datasets for training.

(2): Class: Classification | Seg: Segmentation | Detect: Detection | Gen: Generation | Clust: Clustering | Reg: Registration