Distribution Atlas of COVID-19 Pneumonia on Computed Tomography: A Deep Learning Based Description

Shan Huang; Yuancheng Wang; Zhen Zhou; Qian Yu; Yizhou Yu; Yi Yang; Shenghong Ju

doi:10.1007/s43657-021-00011-4

Distribution Atlas of COVID-19 Pneumonia on Computed Tomography: A Deep Learning Based Description

Phenomics. 2021;1(2):62-72. doi: 10.1007/s43657-021-00011-4. Epub 2021 May 11.

Authors

Shan Huang¹, Yuancheng Wang¹, Zhen Zhou², Qian Yu¹, Yizhou Yu³, Yi Yang⁴, Shenghong Ju¹

Affiliations

¹ Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.
² School of Electronics Engineering and Computer Science, Peking University, Beijing, China.
³ Department of Computer Science, The University of Hong Kong, Hong Kong, China.
⁴ Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.

Abstract

Objectives: To construct a distribution atlas of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) and further explore the difference in distribution by location and disease severity through a retrospective study of 484 cases in Jiangsu, China.

Methods: All patients diagnosed with COVID-19 from January 10 to February 18 in Jiangsu Province, China, were enrolled in our study. The patients were further divided into asymptomatic/mild, moderate, and severe/critically ill groups. A deep learning algorithm was applied to the anatomic pulmonary segmentation and pneumonia lesion extraction. The frequency of opacity on CT was calculated, and a color-coded distribution atlas was built. A further comparison was made between the upper and lower lungs, between bilateral lungs, and between various severity groups. Additional lesion-based radiomics analysis was performed to ascertain the features associated with the disease severity.

Results: A total of 484 laboratory-confirmed patients with 945 repeated CT scans were included. Pulmonary opacity was mainly distributed in the subpleural and peripheral areas. The distances from the opacity to the nearest parietal/visceral pleura were shortest in the asymptomatic/mild group. More diffused lesions were found in the severe/critically ill group. The frequency of opacity increased with increased severity and peaked at about 3-4 or 7-8 o'clock direction in the upper lungs, as opposed to the 5 or 6 o'clock direction in the lower lungs. Lesions with greater energy, more circle-like, and greater surface area were more likely found in severe/critically ill cases than the others.

Conclusion: This study constructed a detailed distribution atlas of COVID-19 pneumonia and compared specific patterns in different parts of the lungs at various severities. The radiomics features most associated with the severity were also found. These results may be valuable in determining the COVID-19 sub-phenotype.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00011-4.

Keywords: COVID-19; Computed tomography; Deep learning; Distribution atlas; Radiomics.