Improving Data Augmentation for Medical Image Segmentation

Zach Eaton-Rosen, Felix Bragman, Sebastien Ourselin, M. Jorge Cardoso

Abstract: Medical image segmentation is often constrained by the availability of labelled training data. ‘Data augmentation’ helps to prevent memorisation of training data and helps the network’s performance on data from outside the training set. As such, it is vital in building robust deep learning pipelines. Augmentation in medical imaging typically involves applying small transformations to images during training to create variety. However, it is also possible to use linear combinations of training images and labels to augment the dataset using the recently-proposed ‘mixup’ algorithm. Here, we apply this algorithm for use in medical imaging segmentation. We show that it increases performance in segmentation tasks, and also offer a theoretical suggestion for the efficacy of this technique.

Author Affiliation: UCL, UCL, KCL, KCL&UCL

Keywords: data augmentation, deep learning, segmentation