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

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.

arXiv commentpaper was accepted to JBHI IEEE journal

References in corpus (6)

• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19
• Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
• Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Cited by in corpus (3)

• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
• COVID-19 Image Data Collection: Prospective Predictions Are the Future
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset