  Deep learning has a great potential to alleviate diagnosis and prognosis for
various clinical procedures. However, the lack of a sufficient number of
medical images is the most common obstacle in conducting image-based analysis
using deep learning. Due to the annotations scarcity, semi-supervised
techniques in the automatic medical analysis are getting high attention.
Artificial data augmentation and generation techniques such as generative
adversarial networks (GANs) may help overcome this obstacle. In this work, we
present an image generation approach that uses generative adversarial networks
with a conditional discriminator where segmentation masks are used as
conditions for image generation. We validate the feasibility of GAN-enhanced
medical image generation on whole heart computed tomography (CT) images and its
seven substructures, namely: left ventricle, right ventricle, left atrium,
right atrium, myocardium, pulmonary arteries, and aorta. Obtained results
demonstrate the suitability of the proposed adversarial approach for the
accurate generation of high-quality CT images. The presented method shows great
potential to facilitate further research in the domain of artificial medical
image generation.
