Biomedical Image Augmentation Using Augmentor.

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Abstract

Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognised due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed.
Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomised elastic distortions. The software has been designed to be highly extensible, meaning an operation that might be specific to a highly specialised task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain.
Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager*.
The GitHub repository contains supplementary information, code examples, and Jupyter notebooks. Extensive documentation is hosted on Read the Docs under https://augmentor.readthedocs.io. For continuous integration tests see https://travis-ci.org/mdbloice/Augmentor.
© The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].