Realistic Adversarial Data Augmentation for MR Image Segmentation
This work addresses data scarcity and privacy issues in medical imaging for researchers and practitioners, offering a plug-in solution for segmentation networks, though it is incremental as it builds on existing adversarial augmentation techniques.
The paper tackles the problem of limited labeled data for medical image segmentation by proposing an adversarial data augmentation method that generates realistic signal corruptions mimicking MR imaging artifacts, specifically bias field, and demonstrates improved generalization, robustness, and performance in low-data scenarios using cardiac MR imaging.
Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.