COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring.
COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring.
复制标题
CXR中的COVID-19：从检测和严重程度评分到患者疾病监测。
DOI：
10.1109/jbhi.2021.3069169
复制
发表时间：
2021-06
影响因子：
7.7
通讯作者：
Greenspan H
中科院分区：
文献类型：
--
作者：
Frid-Adar M;Amer R;Gozes O;Nassar J;Greenspan H
这项工作估计了COVID-19患者肺炎的严重程度，并报告了疾病进展的纵向研究结果。它提出了一种深度学习模型，用于在胸部X射线（CXR）图像中同时检测和定位肺炎，该模型可推广到COVID-19肺炎。定位图用于计算指示疾病严重程度的“肺炎比率”。疾病严重程度的评估用于建立住院患者的时间疾病程度概况。为了验证该模型对患者监测任务的适用性，我们开发了一种验证策略，该策略涉及从连续CT扫描合成数字重建X射线照片（DRR-合成X射线）;然后我们将从DRR生成的疾病进展概况与从CT体积生成的疾病进展概况进行比较。
This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a “Pneumonia Ratio” which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.