The success of neural networks on medical image segmentation tasks typically
relies on large labeled datasets for model training. However, acquiring and
manually labeling a large medical image set is resource-intensive, expensive,
and sometimes impractical due to data sharing and privacy issues. To address
this challenge, we propose AdvChain, a generic adversarial data augmentation
framework, aiming at improving both the diversity and effectiveness of training
data for medical image segmentation tasks. AdvChain augments data with dynamic
data augmentation, generating randomly chained photo-metric and geometric
transformations to resemble realistic yet challenging imaging variations to
expand training data. By jointly optimizing the data augmentation model and a
segmentation network during training, challenging examples are generated to
enhance network generalizability for the downstream task. The proposed
adversarial data augmentation does not rely on generative networks and can be
used as a plug-in module in general segmentation networks. It is
computationally efficient and applicable for both low-shot supervised and
semi-supervised learning. We analyze and evaluate the method on two MR image
segmentation tasks: cardiac segmentation and prostate segmentation with limited
labeled data. Results show that the proposed approach can alleviate the need
for labeled data while improving model generalization ability, indicating its
practical value in medical imaging applications.