USAugment provides ultrasound-specific image transforms for training deep neural networks. It accompanies our article Revisiting Data Augmentation for Ultrasound Images.
Checkout the Wiki to find the documentation, usage examples, and contributing guidelines.
This package can be installed from PyPI by running:
pip install usaugment
Here add a quick code demo showing how to compose a multiple augmentations together using Albumentations after the package is installed.
>>> import albumentations as A
>>> from usaugment.albumentations import DepthAttenuation, GaussianShadow, HazeArtifact, SpeckleReduction
>>> transform = A.Compose(
... [
... DepthAttenuation(p=0.5),
... GaussianShadow(p=0.5),
... HazeArtifact(p=0.5),
... SpeckleReduction(p=0.5),
... ],
... additional_targets={"scan_mask": "mask"}
...)
>>> transformed = transform(image=image)
Checkout the Documentation for more detailed examples and information about scan masks.
There's a notebook in the notebooks directory that provides examples that visualize the effects of each augmentation. You can run these examples using Binder (see the tag at the top of page) or locally by installing the optional examples
dependencies (Matplotlib and Jupyter Notebook):
pip install '.[examples]'
I'd love for this package to grow and flourish into a resource that anyone with an interest in training deep neural networks for ultrasound analysis tasks can pick up and use quickly and easily. Any help addressing bugs, contributing new augmentations, or any other improvements are welcome and appreciated! I only ask that you respect the community guidelines laid out in the CODE_OF_CONDUCT.md
. For more information on how to contribute, checkout out the Documentation.
To ensure that your code meets the style guidelines etc., make sure you install the optional development dependencies:
pip install '.[dev]'
nbstripout --install
pre-commit install
If you use the augmentations in USAugment in your research, please cite our article Revisiting Data Augmentation for Ultrasound Images. This helps more people find and use the package and encourages us to continue maintaining and improving it!
@misc{tupper2025,
title={Revisiting Data Augmentation for Ultrasound Images},
author={Adam Tupper and Christian Gagné},
year={2025},
eprint={2501.13193},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2501.13193},
}