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Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Purpose: To present a method that automatically segments and quantifies ...
06/09/2020 ∙ by Shikha Chaganti, et al. ∙
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Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
Background: Chest computed tomography (CT) is recognized as an important...
03/26/2020 ∙ by Zhenyu Tang, et al. ∙
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Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digita
Purpose: To leverage volumetric quantification of airspace disease (AD) ...
08/13/2020 ∙ by Eduardo Mortani Barbosa Jr., et al. ∙
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Coronavirus Detection and Analysis on Chest CT with Deep Learning
The outbreak of the novel coronavirus, officially declared a global pand...
04/06/2020 ∙ by Ophir Gozes, et al. ∙
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3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
The Coronavirus Disease (COVID-19) has affected 1.8 million people and r...
05/05/2020 ∙ by Siqi Liu, et al. ∙
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Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis
Splits on canned beans appear in the process of preparation and canning....
05/01/2019 ∙ by Yunfei Long, et al. ∙
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Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
Cardiovascular disease (CVD) is the global leading cause of death. A str...
02/12/2019 ∙ by Bob D. de Vos, et al. ∙
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Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
04/02/2020 ∙ by Shikha Chaganti, et al. ∙
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Purpose: To present a method that automatically detects and quantifies abnormal tomographic patterns commonly present in COVID-19, namely Ground Glass Opacities (GGO) and consolidations. Given that high opacity abnormalities (i.e., consolidations) were shown to correlate with severe disease, the paper introduces two combined severity measures (Percentage of Opacity, Percentage of High Opacity) and (Lung Severity Score, Lung High Opacity Score). They quantify the extent of overall COVID-19 abnormalities and the presence of high opacity abnormalities, global and lobe-wise, respectively, being computed based on 3D segmentations of lesions, lungs, and lobes. Materials and Methods: The proposed method takes as input a non-contrasted Chest CT and segments the lesions, lungs, and lobes in 3D. It outputs two combined measures of the severity of lung/lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure (POO, POHO) is global, while the second (LSS, LHOS) is lobe-wise. Evaluation is reported on CTs of 100 subjects (50 COVID-19 confirmed and 50 controls) from institutions from Canada, Europe and US. Ground truth is established by manual annotations of lesions, lungs, and lobes. Results: Pearson Correlation Coefficient between method prediction and ground truth is 0.97 (POO), 0.98 (POHO), 0.96 (LSS), 0.96 (LHOS). Automated processing time to compute the severity scores is 10 seconds/case vs 30 mins needed for manual annotations. Conclusion: A new method identifies regions of abnormalities seen in COVID-19 non-contrasted Chest CT and computes (POO, POHO) and (LSS, LHOS) severity scores.
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