Automated quantification of COVID-19 severity and progression using chest CT images

• Published in: European radiology Vol. 31; no. 1; pp. 436 - 446
• Main Authors: Pu, Jiantao, Leader, Joseph K., Bandos, Andriy, Ke, Shi, Wang, Jing, Shi, Junli, Du, Pang, Guo, Youmin, Wenzel, Sally E., Fuhrman, Carl R., Wilson, David O., Sciurba, Frank C., Jin, Chenwang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2021; Springer Nature B.V
• Subjects: Adult; Algorithms; Automation; Chest; Computed tomography; Computer programs; Computer vision; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Deep Learning; Diagnostic Radiology; Disease Progression; Humans; Image segmentation; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Learning algorithms; Lung - diagnostic imaging; Lungs; Machine learning; Medical imaging; Medical treatment; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Pneumonia; Pneumonitis; Radiology; Retrospective Studies; SARS-CoV-2; Software; Subjective assessment; Tomography, X-Ray Computed - methods; Ultrasound; Vessels; COVID-19; Biomarkers; Pneumonia; Neural network
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-020-07156-2

Objective To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. Methods One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. Results There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76–86%). In detecting large pneumonia regions (> 200 mm 3 ), the algorithm had a sensitivity of 95% (CI 94–97%) and specificity of 84% (CI 81–86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least “acceptable” for representing disease progression. Conclusion The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. Key Points • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.