论文标题
肺部感染和正常区域分割，来自Covid-19病例的CT体积
Lung infection and normal region segmentation from CT volumes of COVID-19 cases
论文作者
论文摘要
本文提出了一种自动化的分割方法，即从COVID-19患者的CT体积中的肺中的感染和正常区域。从2019年12月开始，2019年新型冠状病毒疾病（Covid-19）在世界范围内传播，并对我们的经济活动和日常生活产生了重大影响。为了诊断大量感染患者，需要对计算机进行诊断帮助。胸部CT可有效诊断病毒性肺炎，包括COVID-19。 COVID-19的诊断辅助需要一种计算机CT体积的肺状况的定量分析方法。本文提出了一种使用COVID-19分割完全卷积网络（FCN）的CT体积中感染和正常区域的自动分割方法。在诊断包括Covid-19在内的肺部疾病时，对肺正常和感染区域的条件分析很重要。我们的方法识别和段肺正常和CT体积的感染区域。对于具有各种形状和尺寸的细分感染区域，我们在FCN中引入了密集的合并连接和扩张的卷积。我们将提出的方法应用于COVID-19病例的CT体积。从轻度到严重的COVID-19，该方法正确分段了肺中的正常和感染区域。正常和感染区域的骰子评分分别为0.911和0.753。
This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease 2019 (COVID-19) spreads over the world and giving significant impacts to our economic activities and daily lives. To diagnose the large number of infected patients, diagnosis assistance by computers is needed. Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A quantitative analysis method of condition of the lung from CT volumes by computers is required for diagnosis assistance of COVID-19. This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes using a COVID-19 segmentation fully convolutional network (FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions of normal and infection regions in the lung is important. Our method recognizes and segments lung normal and infection regions in CT volumes. To segment infection regions that have various shapes and sizes, we introduced dense pooling connections and dilated convolutions in our FCN. We applied the proposed method to CT volumes of COVID-19 cases. From mild to severe cases of COVID-19, the proposed method correctly segmented normal and infection regions in the lung. Dice scores of normal and infection regions were 0.911 and 0.753, respectively.
