Biomedical image augmentation using Augmentor

Marcus Daniel Bloice, Peter M. Roth, Andreas Holzinger

Research output: Contribution to journalArticlepeer-review


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 …
Original languageEnglish
Pages (from-to)4522-4524
Issue number21
Publication statusPublished - 2019


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