  Echocardiography (echo) is a common means of evaluating cardiac conditions.
Due to the label scarcity, semi-supervised paradigms in automated echo analysis
are getting traction. One of the most sought-after problems in echo is the
segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an
echocardiogram generation approach using generative adversarial networks with a
conditional patch-based discriminator. In this work, we validate the
feasibility of GAN-enhanced echo generation with different conditions
(segmentation masks), namely, the left ventricle, ventricular myocardium, and
atrium. Results show that the proposed adversarial algorithm can generate
high-quality echo frames whose cardiac structures match the given segmentation
masks. This method is expected to facilitate the training of other machine
learning models in a semi-supervised fashion as suggested in similar
researches.

Image and Video Processing;Machine Learning;Machine Learning