J Korean Med Sci.  2021 Feb;36(5):e46. 10.3346/jkms.2021.36.e46.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective

• Kang M
• Hong KS
• Chikontwe P
• Luna M
• Jang JG
• Park J
• Shin KC
• Park SH
• Ahn JH

• KMID: 2512468
• DOI: http://doi.org/10.3346/jkms.2021.36.e46

Abstract

Background
It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition.
Methods
This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups.Each lesion patch cluster was described by a characteristic imaging term for comparison.For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity.
Results
The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters.
Conclusion
Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Keyword

COVID-19; Pneumonia; Bacterial; Computed Tomography; Deep Learning; Cluster Analysis

Figure

• 

  Fig. 1 Flow chart.COVID-19 = coronavirus disease 2019, CT = computed tomography.

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  Fig. 2 Diagram of the modeling framework. For analyzing the imaging patterns, the segmentation models were employed to extract regions of interest of the lung and lesions in the chest CT scans. Then, the ResNet50 model takes lesion cropped patches as input and returns a label prediction and a 2048-dimensional feature vector. Next, the K-means clustering algorithm was used to cluster 2D lesion features into 20 groups. To verify the usability of these patterns, (i) majority voting; (ii) constructed histogram with SVM; and (iii) Radiomics features (with/without constructed histogram) with SVM were employed. Disease and severity classification tasks were performed.COVID-19 = coronavirus disease 2019, CT = computed tomography, SVM = Support Vector Machine.

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  Fig. 3 Visualization of the lesion patches from 170 patients into two-dimensions. (A) K-means clusters (20 groups) were used for distinction. Different color edge represents different groups. (B) True diagnosis was used for distinction. Purple color represents non-severe COVID-19, magenta represents severe COVID-19, and green for bacterial pneumonia.COVID-19 = coronavirus disease 2019.

• 

  Fig. 4 (A) Typical COVID-19 CT manifestations—GGOs with interlobular septal thickening in the peripheral lungs—were observed in cluster 4 and 17. (B) bacterial pneumonia CT manifestation—crazy-paving appearance in the posterior lungs—observed in cluster 5 and 10. (C) Cluster 8 showed the lowest t-test P value < 0.001 for diseased and severe groups which is the typical pattern for severe COVID-19 patients (i.e., diffuse GGOs in the central and peripheral lungs). (D) Bacterial pneumonia CT manifestation—extensive consolidation with air-bronchogram—was observed in cluster 14. These clusters showed a t-test P value < 0.001. Lesions were colored based on K-means clustering result.COVID-19 = coronavirus disease 2019, CT = computed tomography, GGO = ground-glass opacity.

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  Fig. 5 Mean histograms. (A) Mean histogram of COVID-19; (B) Mean histogram of bacterial pneumonia; (C) Mean histogram of severe COVID-19 patients; and (D) Mean histogram of non-severe COVID-19 patients.COVID-19 = coronavirus disease 2019.

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  Fig. 6 Representative slices which showed an average dice coefficient score (78.06%) for lesion segmentation. (A) Slice showed a dice coefficient score of 77.08%. (B) Slice showed a dice coefficient score of 78.31%. Red color represents a manual segmentation by human and green color represents an automated segmentation by the trained segmentation model. Due to the ambiguity of lesion boundaries, an average dice score of 78.06% showed satisfactory lesion segmentation results for the deep feature extractor.

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  Fig. 7 Comparisons between values of cluster 8 versus NEWS (A) and disease severity (B) in COVID-19 patients.NEWS = National Early Warning Score, COVID-19 = coronavirus disease 2019.

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