Semi-supervised task-driven data augmentation for medical image segmentation

•We present a novel task-driven and semi-supervised data augmentation scheme to improve medical image segmentation performance in a limited data setting.•In the proposed method, we design two conditional generative models to output two sets of transformations, namely deformation fields and additive intensity masks, to model shape and intensity characteristics, respectively.•The generated transformations are optimized for segmentation task performance (task-driven nature), and unlabeled data is leveraged in the generative process (semi-supervised nature).•We evaluated the proposed method on three datasets, namely cardiac, prostate, and pancreas, and obtained substantial performance gains over compared methods.