COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

• URL: http://arxiv.org/abs/2008.02150v2
• Date: Wed, 31 Mar 2021 09:37:30 GMT
• Title: COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
• Authors: Rula Amer, Maayan Frid-Adar, Ophir Gozes, Jannette Nassar, Hayit Greenspan
• Abstract summary: We developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray images. 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.
• Score: 3.9373541926236766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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