Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
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DOI：
10.1148/ryai.2020200048
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发表时间：
2020-07-01
期刊：
影响因子：
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通讯作者：
Comaniciu, Dorin
中科院分区：
文献类型：
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作者：
Chaganti, Shikha;Grenier, Philippe;Comaniciu, Dorin
目的：提出一种自动分割和量化新冠肺炎中常见的异常CT模式，即毛玻璃样阴影和实变的方法。材料和方法：在这项回顾性研究中，该方法以胸部平扫CT为输入，基于9749个胸部CT体积的数据集，对病变、肺和肺叶进行三维分割。该方法基于深度学习和深度强化学习，输出肺和肺叶受累严重程度的两个组合测量，量化新冠肺炎异常的程度和高混浊的存在。第一个测量(不透明百分比，高不透明百分比[PO，pho])是全局性的，而第二个(肺严重程度评分，肺高不透明度评分[LSS，LHOS])是叶状的。据报道，在2002年至2020年4月期间收集的来自加拿大、欧洲和美国的机构的200名参与者(100名新冠肺炎确诊患者和100名健康对照)的CT研究中，对该算法进行了评估。基本事实是通过对病变、肺和肺叶的手动注释来确定的。结果：新冠肺炎事件的预测结果与实际情况的皮尔逊相关系数为0.92(P
Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in COVID-19, namely ground-glass opacities and consolidations.Materials and Methods: In this retrospective study, the proposed method takes as input a noncontrast chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (percentage of opacity, percentage of high opacity [PO, PHO]) is global, while the second of (lung severity score, lung high opacity score [LSS, LHOS]) is lobe-wise. Evaluation of the algorithm is reported on CT studies of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe, and the United States collected between 2002 and April 2020. Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.Results: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P