Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

中文翻译：

---

使用基于混合弱标签的深度学习从 CT 图像中对 COVID-19 的严重性和整合进行量化


冠状病毒病 (COVID-19) 的早期、准确诊断对于患者隔离和接触者追踪至关重要，从而限制感染的传播。计算机断层扫描 (CT) 可以提供有关 COVID-19 的重要信息，特别是对于患有中度至重度疾病以及心肺状况恶化的患者。作为一种自动工具，深度学习方法可用于对受影响的肺部区域进行语义分割，这对于建立疾病严重程度和预后预测非常重要。肺部混浊的程度和类型都有助于评估疾病的严重程度。然而，手动像素级多类标记非常耗时、主观且非定量。在本文中，我们提出了一种基于混合弱标签的深度学习方法，该方法利用了来自 COVID-19 肺炎的手动注释的肺部混浊和临床报告中提供的患者级疾病类型信息。 UNet 首先使用语义标签进行训练，以分割整个感染区域。它用于初始化另一个 UNet，该 UNet 被训练为使用期望最大化 (EM) 算法对患者级别信息的合并进行分段。为了证明所提出方法的性能，使用了来自伊朗、意大利、韩国和美国的多机构 CT 数据集。结果表明，我们提出的方法可以预测感染区域以及合并区域，与人类注释具有良好的相关性。