  Conventional data augmentation realized by performing simple pre-processing
operations (\eg, rotation, crop, \etc) has been validated for its advantage in
enhancing the performance for medical image segmentation. However, the data
generated by these conventional augmentation methods are random and sometimes
harmful to the subsequent segmentation. In this paper, we developed a novel
automatic learning-based data augmentation method for medical image
segmentation which models the augmentation task as a trial-and-error procedure
using deep reinforcement learning (DRL). In our method, we innovatively combine
the data augmentation module and the subsequent segmentation module in an
end-to-end training manner with a consistent loss. Specifically, the best
sequential combination of different basic operations is automatically learned
by directly maximizing the performance improvement (\ie, Dice ratio) on the
available validation set. We extensively evaluated our method on CT kidney
tumor segmentation which validated the promising results of our method.
