Purpose

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.

comparison
Name Repository BIDS DL base library Task (segmentation, detection, classification) Data dimension Multichannel Multilabel Uncertainty Transfer Learning Pre-processing tools Post-processing tools User case examples Multi-GPU data parallelism Automatic Model evaluation Input region of interest Missing modality Model performance comparison Automatic hyperparameter optimisation Packaged Multi-center Model
ivadomed https://github.com/ivadomed/ivadomed

PyTorch Classification, Segmentation, Detection 2D, 3D

monai https://github.com/Project-MONAI/MONAI

PyTorch Segmentation, Classification 2D, 3D

delira https://github.com/justusschock/delira

PyTorch and TensorFlow Classification, Generation, Segmentation 2D, 3D

MIC-DKFZ https://github.com/MIC-DKFZ/medicaldetectiontoolkit

PyTorch Detection 2D, 3D

ANTsPyNet https://github.com/ANTsX/ANTsPyNet

Tensorflow/Keras Classification, Segmentation, Clustering, GAN, Registration, Super-resolution, Autoencoder 2D, 3D

DLTK https://github.com/DLTK/DLTK

Tensorflow Classification, Segmentation, GAN, Registration, Super-resolution, Autoencoder 3D

MIScnn https://github.com/frankkramer-lab/MIScnn

Tensorflow/Keras Segmentation 2D, 3D

niftytorch https://niftytorch.github.io/doc/

PyTorch Classification, Segmentation 3D

DeepNeuro https://github.com/QTIM-Lab/DeepNeuro

Tensorflow/Keras Segmentation 2D, 3D

(*): Brain Imaging Data Structure