  Supervised deep learning methods for segmentation require large amounts of
labelled training data, without which they are prone to overfitting, not
generalizing well to unseen images. In practice, obtaining a large number of
annotations from clinical experts is expensive and time-consuming. One way to
address scarcity of annotated examples is data augmentation using random
spatial and intensity transformations. Recently, it has been proposed to use
generative models to synthesize realistic training examples, complementing the
random augmentation. So far, these methods have yielded limited gains over the
random augmentation. However, there is potential to improve the approach by (i)
explicitly modeling deformation fields (non-affine spatial transformation) and
intensity transformations and (ii) leveraging unlabelled data during the
generative process. With this motivation, we propose a novel task-driven data
augmentation method where to synthesize new training examples, a generative
network explicitly models and applies deformation fields and additive intensity
masks on existing labelled data, modeling shape and intensity variations,
respectively. Crucially, the generative model is optimized to be conducive to
the task, in this case segmentation, and constrained to match the distribution
of images observed from labelled and unlabelled samples. Furthermore, explicit
modeling of deformation fields allow synthesizing segmentation masks and images
in exact correspondence by simply applying the generated transformation to an
input image and the corresponding annotation. Our experiments on cardiac
magnetic resonance images (MRI) showed that, for the task of segmentation in
small training data scenarios, the proposed method substantially outperforms
conventional augmentation techniques.
