  Supervised learning-based segmentation methods typically require a large
number of annotated training data to generalize well at test time. In medical
applications, curating such datasets is not a favourable option because
acquiring a large number of annotated samples from experts is time-consuming
and expensive. Consequently, numerous methods have been proposed in the
literature for learning with limited annotated examples. Unfortunately, the
proposed approaches in the literature have not yet yielded significant gains
over random data augmentation for image segmentation, where random
augmentations themselves do not yield high accuracy. In this work, we propose a
novel task-driven data augmentation method for learning with limited labeled
data where the synthetic data generator, is optimized for the segmentation
task. The generator of the proposed method models intensity and shape
variations using two sets of transformations, as additive intensity
transformations and deformation fields. Both transformations are optimized
using labeled as well as unlabeled examples in a semi-supervised framework. Our
experiments on three medical datasets, namely cardic, prostate and pancreas,
show that the proposed approach significantly outperforms standard augmentation
and semi-supervised approaches for image segmentation in the limited annotation
setting. The code is made publicly available at
https://github.com/krishnabits001/task$\_$driven$\_$data$\_$augmentation.
