Enhancing MR image segmentation with realistic adversarial data augmentation

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. [Display omitted] •Presenting a generic, practical adversarial data augmentation framework (AdvChain) for medical image segmentation tasks.•Supporting effective optimization of dynamic compositions of photo-metric and geometric transformations to generate realistic hard examples for improved model generalization.•A computationally efficient yet powerful plug-in module to support both supervised and semi-supervised learning even when labeled data is very limited.•Achieving comparable or superior performance to competitive data augmentation methods such as RandAugment.