# ivadomed: A Medical Imaging Deep Learning Toolbox

arXiv: https://arxiv.org/abs/2010.09984 · HTML: https://mlsanity.com/paper/2010.09984/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Label fusion and training methods for reliable representation of inter-rater uncertainty](https://mlsanity.com/paper/2202.07550/) (2022)
- [Impact of individual rater style on deep learning uncertainty in medical imaging segmentation](https://mlsanity.com/paper/2105.02197/) (2021)
- [Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling](https://mlsanity.com/paper/2108.06554/) (2021)
- [Benefits of Linear Conditioning with Metadata for Image Segmentation](https://mlsanity.com/paper/2102.09582/) (2021)
- [Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge](https://mlsanity.com/paper/2109.05409/) (2021)
- [An overview of open source Deep Learning-based libraries for Neuroscience](https://mlsanity.com/paper/2301.05057/) (2022)
- [Metadata Improves Segmentation Through Multitasking Elicitation](https://mlsanity.com/paper/2308.09411/) (2023)
- [2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T](https://mlsanity.com/paper/2110.06516/) (2021)

## Abstract (arXiv)

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, command-line 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 metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) 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 without the need to manually organize the data, which is typically a tedious task. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step is conveniently configurable via a single file. At the same time, the code is highly modular to allow addition/modification of an architecture or pre/post-processing steps. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. Overall, ivadomed enables easy and quick exploration of the latest advances in deep learning for medical imaging applications. ivadomed's main project page is available at https://ivadomed.org.