History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98 , 0.73 , 0.71 , 0.73 , 0.71 , 0.71 , 0.57 , 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

中文翻译：

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使用 CT 扫描图像自动进行 COVID-19 肺部感染区域分割和测量

历史表明，传染病 (COVID-19) 可以迅速震惊世界，对健康造成巨大损失，从安全和经济角度对数十亿人的生活产生深远影响，以控制 COVID- 19大流行。最好的策略是提供早期干预以阻止疾病的传播。通常，计算机断层扫描 (CT) 用于检测肺炎、肺部、肺结核、肺气肿或其他胸膜（覆盖肺部的膜）疾病中的肿瘤。CT 成像系统的缺点是： 与 MRI 相比，软组织对比度较差，因为它是基于 X 射线的辐射暴露。肺部CT图像分割是肺部图像分析的必要初始步骤。由于强度不均匀性、伪影的存在，分割算法的主要挑战被夸大了，和不同软组织灰度的接近程度。本文的目标是设计和评估一种使用胸部 CT 图像自动进行 COVID-19 肺部感染分割和测量的自动工具。广泛的计算机模拟表明，与最先进的分割方法（即 GraphCut、医学图像分割 (MIS) 和 Watershed）相比，这种端到端的 CT 图像分割学习方法具有更好的效率和灵活性。在 COVID-CT 数据集上进行的实验包含 (275) 个对 COVID-19 呈阳性的 CT 扫描和从 EL-BAYANE 放射学和医学成像中心获取的新数据。使用准确度、灵敏度、F-measure、精度、MCC、Dice、Jacquard 和特异性获得的统计测量的均值是 0.98、0.73、0.71、0.73、0.71、0。分别为 71 、 0.57 、 0.99 ；这比上面提到的方法更好。取得的结果证明，所提出的方法更加稳健、准确和直接。