论文标题
CXR中的Covid-19：从检测和严重程度评分到患者疾病监测
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
论文作者
论文摘要
在这项工作中，我们估计了199例COVID患者肺炎的严重程度，并对疾病进展进行纵向研究。为了实现这一目标，我们开发了一种深度学习模型，用于同时检测和分割胸部XRAR（CXR）图像，并概括为Covid-19-19-19。该分段用于计算“肺炎比”，该比例表明疾病的严重程度。疾病严重程度的测量使住院患者可以随着时间的流逝而建立疾病程度。为了验证与患者监测任务相关的模型，我们制定了一种验证策略，该策略涉及从串行CT扫描中合成数字重建的X光片（DRRS-合成XRAY）；然后，我们比较了从DRR产生的疾病进展谱与CT体积产生的疾病进展谱。
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a "Pneumonia Ratio" which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance 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.
