Data Augmentation via Image Registration

Abstract

Data augmentation helps improve generalization of deep neural networks, and can be perceived as implicit regularization. It is pivotal in scenarios in which the amount of ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a common problem in medical image analysis, especially tumor delineation—in this paper, we focus on brain-tumor segmentation from magnetic resonance imaging (MRI), and propose a novel augmentation technique which exploits image registration to benefit from subtle spatial and/or tissue characteristics captured within the training set. We used a set of MRI scans of 44 low-grade glioma patients, augmented it using the proposed technique, and exploited it to train U-Net-based deep networks. The results show that our augmentation delivers statistically important boost of performance without sacrificing inference speed.