CT Imaging Changes of Corona Virus Disease 2019 (COVID-19): A multi-center study in Southwest China

Li, Xiaoming; Zeng, Wenbing; Li, Xiang; Chen, Haonan; Shi, Linping; Li, Xinghui; Xiang, Hongnian; Cao, Yang; Chen, Hui; Liu, Chen; Wang, Jian

doi:10.21203/rs.3.rs-16974/v1

Cited by 74 publications

(105 citation statements)

References 12 publications

(15 reference statements)

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“…Although the imaging presentation of COVID-19 is similar to that of other viral pneumonias, the differential diagnosis is more di cult. However, COVID-19 could manifest some characteristic imaging signs, especially when groundglass opacities were present [12,13].…”

Section: Discussionmentioning

confidence: 99%

A Comparative Study of Target Reconstruction of Ultra-High-Resolution CT for Patients with Corona-Virus Disease 2019 (COVID-19)

Lin

Kang

et al. 2020

Preprint

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Background: The corona-virus disease 2019 (COVID-19) pandemic has caused a serious public health risk. Compared with conventional high-resolution CT (C-HRCT, matrix 512), ultra-high resolution CT (U-HRCT, matrix 1024) can increase the effective pixel per unit volume by about 4 times. Our study is to evaluate the value of target reconstruction of U-HRCT in the accurate diagnosis of COVID-19. Methods: A total of 13 COVID-19 cases, 44 cases of other pneumonias, and 6 cases of ground-glass nodules were retrospectively analyzed. The data were categorized into groups A (C-HRCT) and B (U-HRCT), following which iDose4-3 and iDose4-5 were used for target reconstruction, respectively. CT value, noise, and signal-to-noise ratio (SNR) in different reconstructed images were measured. Two senior imaging doctors scored the image quality and the structure of the lesions on a 5-point scale. Chi-square test, variance analysis, and binary logistic regression analysis were used for statistical analysis. Results: U-HRCT image can reduce noise and improve SNR with an increase of the iterative reconstruction level. The SNR of U-HRCT image was lower than that of the C-HRCT image of the same iDose4 level, and the noise of U-HRCT was higher than that of C-HRCT image; the difference was statistically significant (P < 0.05). Logistic regression analysis showed that peripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern were independent indictors of the COVID-19 on U-HRCT. U-HRCT was superior to C-HRCT in showing the blood vessels, bronchial wall, and interlobular septum in the ground-glass opacities; the difference was statistically significant (P < 0.05). Conclusions: Peripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern on U-HRCT are favorable signs for COVID-19. U-HRCT is superior to C-HRCT in displaying the blood vessels, bronchial walls, and interlobular septum for evaluating COVID-19.

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Section: Discussionmentioning

confidence: 99%

A Comparative Study of Target Reconstruction of Ultra-High-Resolution CT for Patients with Corona-Virus Disease 2019 (COVID-19)

Lin

Kang

et al. 2020

Preprint

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“…Then 13 statistical texture features are extracted based on the grey level co-occurrence matrix. In contrast with GLCM, GLGCM captures not only gray scale features but also the second order statistics of gray level gradients while gradients indicate the information of image edge which provides signi cant features of an image [6]. In addition, the histological characteristics of COVID-19 and GP can be well re ected in the gray mode, and the gray histogram is an intuitive statistical method 27 .…”

Section: Feature Extractionmentioning

confidence: 99%

“…,6,7,8,9,10,14,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32 4 0.12 6,8,9,18,19,20,21,22,23,25,26,27,28,29,31,32 5 0.116,8,9,14,18,19,20,21,22,23,24,25,26,27,28,29,31,32 *select features with relevance smaller than the threshold T.…”

mentioning

confidence: 99%

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Liu

Wang

Liu

et al. 2020

Preprint

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Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

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“…Though clinical symptoms such as consolidations and ground-glass opacities [13] are more accurately recognizable in Computed Tomography (CT) scans, CXR's could still provide a coarse and cheap bed-side indication of such symptoms if these visualizations are enhanced by labels and clinical notes from radiologists and domain experts. Fig.…”

Section: Coarse Region Localization Map With Gradient Class Activationmentioning

confidence: 99%

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Zebin

Rezvy²,

Pang³

2020

Preprint

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Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

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CT Imaging Changes of Corona Virus Disease 2019 (COVID-19): A multi-center study in Southwest China

Cited by 74 publications

References 12 publications

A Comparative Study of Target Reconstruction of Ultra-High-Resolution CT for Patients with Corona-Virus Disease 2019 (COVID-19)

A Comparative Study of Target Reconstruction of Ultra-High-Resolution CT for Patients with Corona-Virus Disease 2019 (COVID-19)

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

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