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 mediccal 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.

中文翻译：

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使用条件生成对抗网络进行 COVID-19 CT 图像合成


2019 年冠状病毒病 (COVID-19) 是一种持续的全球流行病，自 2019 年 12 月以来迅速传播。实时逆转录聚合酶链反应 (rRT-PCR) 和胸部计算机断层扫描 (CT) 成像在 COVID-19 中都发挥着重要作用。 19 诊断。胸部 CT 成像具有报告快、成本低、肺部感染检测灵敏度高的优点。最近，基于深度学习的计算机视觉方法在医学成像应用（包括 X 射线、磁共振成像和 CT 成像）中显示出巨大的应用前景。然而，训练深度学习模型需要大量数据，并且由于该疾病的高传染性，医务人员在收集COVID-19 CT数据时面临很高的风险。另一个问题是缺乏可用于数据标记的专家。为了满足COVID-19 CT成像的数据要求，我们提出了一种基于条件生成对抗网络的CT图像合成方法，可以有效生成高质量且逼真的COVID-19 CT图像，用于基于深度学习的医学成像任务。实验结果表明，所提出的方法在生成的 COVID-19 CT 图像上优于其他最先进的图像合成方法，并表明在包括语义分割和分类在内的各种机器学习应用中具有广阔的前景。