3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
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发表时间：
2020-05
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通讯作者：
Siqi Liu;B. Georgescu;Zhoubing Xu;Y. Yoo;G. Chabin;S. Chaganti;Sasa Grbic;Sebastian Piat;
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作者：
Siqi Liu;B. Georgescu;Zhoubing Xu;Y. Yoo;G. Chabin;S. Chaganti;Sasa Grbic;Sebastian Piat;
截至二零二零年四月十二日，冠状病毒病（COVID-19）已影响180万人，并导致超过11万人死亡。一些研究表明，胸部计算机断层扫描（CT）上看到的断层扫描模式，如磨玻璃样阴影，实变和疯狂的铺路图案，与疾病的严重程度和进展相关。因此，CT成像可以成为COVID-19患者管理的重要方式。基于AI的解决方案可用于支持基于CT的定量报告，并在可自动计算定量生物标志物（如不透明度百分比（PO））的情况下使阅读高效且可重复。然而，COVID-19给人工智能的发展带来了独特的挑战，特别是在大规模提供适当的图像数据和注释方面。在本文中，我们建议使用合成数据集来增强现有的COVID-19数据库，以应对这些挑战。我们训练了一个生成对抗网络（GAN），以在没有感染性疾病的患者的胸部CT上修复COVID-19相关的断层扫描模式。此外，我们利用来自手动标记的COVID-19胸部CT患者的位置先验来生成适当的异常分布。通过将20%的合成数据添加到真实的COVID-19训练数据中，合成数据用于改进肺部分割和COVID-19模式的分割。我们收集了2143例胸部CT，其中327例COVID-19阳性病例，来自7个国家的12个研究中心。通过对100例COVID-19阳性病例和100例对照病例进行测试，我们发现合成数据可以帮助改善肺部分割（+6.02%病变包含率）和异常分割（+2.78%骰子系数），从而使PO计算总体上更准确（+2.82%皮尔逊系数）。
The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Several studies have shown that tomographic patterns seen on chest Computed Tomography (CT), such as ground-glass opacities, consolidations, and crazy paving pattern, are correlated with the disease severity and progression. CT imaging can thus emerge as an important modality for the management of COVID-19 patients. AI-based solutions can be used to support CT based quantitative reporting and make reading efficient and reproducible if quantitative biomarkers, such as the Percentage of Opacity (PO), can be automatically computed. However, COVID-19 has posed unique challenges to the development of AI, specifically concerning the availability of appropriate image data and annotations at scale. In this paper, we propose to use synthetic datasets to augment an existing COVID-19 database to tackle these challenges. We train a Generative Adversarial Network (GAN) to inpaint COVID-19 related tomographic patterns on chest CTs from patients without infectious diseases. Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions. Synthetic data are used to improve both lung segmentation and segmentation of COVID-19 patterns by adding 20% of synthetic data to the real COVID-19 training data. We collected 2143 chest CTs, containing 327 COVID-19 positive cases, acquired from 12 sites across 7 countries. By testing on 100 COVID-19 positive and 100 control cases, we show that synthetic data can help improve both lung segmentation (+6.02% lesion inclusion rate) and abnormality segmentation (+2.78% dice coefficient), leading to an overall more accurate PO computation (+2.82% Pearson coefficient).