  With the advent of Deep Learning (DL) techniques, especially Generative
Adversarial Networks (GANs), data augmentation and generation are quickly
evolving domains that have raised much interest recently. However, the DL
techniques are data demanding and since, medical data is not easily accessible,
they suffer from data insufficiency. To deal with this limitation, different
data augmentation techniques are used. Here, we propose a novel unsupervised
data-driven approach for data augmentation that can generate 2D Computed
Tomography (CT) images using a simple GAN. The generated CT images have good
global and local features of a real CT image and can be used to augment the
training datasets for effective learning. In this proof-of-concept study, we
show that our proposed solution using GANs is able to capture some of the
global and local CT variabilities. Our network is able to generate visually
realistic CT images and we aim to further enhance its output by scaling it to a
higher resolution and potentially from 2D to 3D.
