Ivadomed : a medical imaging deep learning toolbox

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, commandline tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image and subject metadata for custom data splitting or extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS) convention will be compatible with ivadomed. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM (Perez et al., 2017) and HeMis (Havaei et al., 2016), as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. ivadomed’s main project page is available at https://ivadomed.org.