Coronavirus disease 2019, COVID-19, has recently gained global proportions (1–3). This short report illustrates the use of voxel-level deep learning–based CT segmentation of pulmonary opacities (4) for improving quantification of the disease. A separate set of CT images from 10 cases of COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction test results was selected for training purposes. Expert manual segmentation of the lungs and pulmonary opacities was used as reference. A convolutional neural network based on U-Net architecture (5) was developed to predict the expert segmentation. We used this pipeline to analyze the contrasting evolution of two confirmed cases of COVID-19 from Wuhan, China, that were receiving similar supportive therapy. Figure 1 shows the favorable evolution of a 48-year-old woman imaged at four time points across an interval of 16 days, while Figure 2 shows the case of a 44-year-old man with disease progression over 12 days, especially between the second and third studies. These examples illustrate the potential of deep learning–based quantitative CT for providing objective assessment of pulmonary involvement and therapy response in COVID-19, but further studies are still necessary to determine the performance of such an approach in this scenario.
Footnotes
Disclosures of Conflicts of Interest: Y.C. disclosed no relevant relationships. Z.X. disclosed no relevant relationships. J.F. disclosed no relevant relationships. C.J. disclosed no relevant relationships. X.H. disclosed no relevant relationships. H.W. disclosed no relevant relationships. H.S. disclosed no relevant relationships.
References
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