ABSTRACT
Data augmentation is a popular technique for reducing overfitting and improving generalization capabilities of deep neural networks. Augmentation encompasses a suite of techniques that enhances the size and diversity of training datasets. It plays a critical role when the amount of high-quality ground truth data is limited, and acquiring new examples is costly and time consuming, a very common problem in medical image analysis, including auto-segmentation for radiation therapy. This chapter reviews current advances in data augmentation techniques applied to auto-segmentation in radiation oncology, including geometric transformations, intensity transformation, and artificial data generation. In addition, an example application of these data augmentation methods for training deep neural networks for segmentation in the domain of radiation therapy is provided.
