  Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has
spread rapidly since December 2019. Real-time reverse transcription polymerase
chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play
an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits
of quick reporting, a low cost, and high sensitivity for the detection of
pulmonary infection. Recently, deep-learning-based computer vision methods have
demonstrated great promise for use in medical imaging applications, including
X-rays, magnetic resonance imaging, and CT imaging. However, training a
deep-learning model requires large volumes of data, and medical staff faces a
high risk when collecting COVID-19 CT data due to the high infectivity of the
disease. Another issue is the lack of experts available for data labeling. In
order to meet the data requirements for COVID-19 CT imaging, we propose a CT
image synthesis approach based on a conditional generative adversarial network
that can effectively generate high-quality and realistic COVID-19 CT images for
use in deep-learning-based medical imaging tasks. Experimental results show
that the proposed method outperforms other state-of-the-art image synthesis
methods with the generated COVID-19 CT images and indicates promising for
various machine learning applications including semantic segmentation and
classification.
