A review of medical image data augmentation techniques for deep learning applications

Abstract

Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

Plan quality in radiotherapy treatment planning – Review of the factors and challenges

“…Modern machine learning and artificial intelligence algorithms are rapidly improving medical image optimisation and analysis and also approaches to target and OAR segmentation, 106 which undoubtedly will contribute to improved treatment plan quality through better images and more consistent structure delineation 107,108 …”

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

“…The inherent need for large amounts of data in deep learning networks has encouraged the development of many strategies ranging from simple transformations such as geometric transformations to complex images composed of mosaics. Among the most commonly used techniques [19], [53]…”

“…The methods are classified as basic and/or deformable and represent about 86% of the data augmentation methods applied in medical imaging for deep learning [53]. Each method listed is described in detail in Appendix B.…”

Survey on Videos Data Augmentation for Deep Learning Models

“…Reviews on image data augmentation for DL models have already been published [19][20][21][22][23]. In their paper, Shorten et al [19] realised a complete survey on image data augmentation for DL, covering both basic image manipulation and DL approaches.…”

“…In face data augmentation, likewise for general image data augmentation, generative models have recently been the primary choice, replacing or enhancing most of the other methods. A different application area was tackled by Chlap et al [22] in their review of medical image data augmentation. The paper analyses the state of the art of CT and MRI image data augmentation for DL applications.…”