Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

Hazem Abuzeid Yousef,Ehab Mansour Mohmad Moussa,Medhat Araby Khalil Saleh,Alaa El-Din Abdel-Moneim El-Sayed,Maha Mohamed Said Ahmed El-Kholy,Mohamed Zidan Mohamed Abdel-Razek,Lamiaa Hasan Shaaban Hasan,Mohamed Karim Mahmoud Omar

doi:10.1186/s43055-021-00602-1

Hazem Abuzeid Yousef, Ehab Mansour Mohmad Moussa + Show 6 more

Open Access

https://doi.org/10.1186/s43055-021-00602-1

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Abstract

BackgroundChest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.ResultsThe Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).ConclusionsThe automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

Highlights

Chest computed tomography (CT) has proven its critical importance in detection, grading, and followup of lung affection in COVID-19 pneumonia
Based on the clinical presentation, the patients were divided into three groups: mild illness: individuals who have any of the various signs and symptoms of covid-19 but who do not have shortness of breath, dyspnea, or abnormal chest imaging findings; moderate illness: individuals who show evidence of lower respiratory tract disease during clinical assessment or imaging and who have an oxygen saturation (Peripheral oxygen saturation (SpO2)) ≥ 94% on room air; severe illness: individuals who have spO2 < 94% on room air, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/Fractional inspired oxygen (FiO2)) < 300 mm hg, respiratory frequency > 30 breaths/min, or lung infiltrates > 50% [38]
The CT features which were observed in patients of the study were as follows (Table 2): most of the patients presented with groundglass opacities (149 patients, 97.3%); alveolar consolidation was encountered in 67 patients (43.7%); crazy-paving pattern in patients (16.8%); reversed halo sign in 4 patients (6.1%); and pleural effusion in (18.2%)

Summary

Introduction

Chest computed tomography (CT) has proven its critical importance in detection, grading, and followup of lung affection in COVID-19 pneumonia. This study aims to explore automated histogram-based quantifica‐ tion of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. CT has proven critical importance in detection, grading, and follow-up of lung affection in this potentially fatal disease. The important role of the radiologists in COVID19 infection is to provide early diagnosis, distinguish the disease from other conditions with similar CT findings and determine disease severity [7, 8]

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