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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 ...
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Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
Purpose: To present a method that automatically detects and quantifies a...
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How Reliable are Test Numbers for Revealing the COVID-19 Ground Truth and Applying Interventions?
The number of confirmed cases of COVID-19 is often used as a proxy for t...
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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...
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COVID-19 Chest CT Image Segmentation – A Deep Convolutional Neural Network Solution
A novel coronavirus disease 2019 (COVID-19) was detected and has spread ...
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Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection Patient Monitoring using Deep Learning CT Image Analysis
Purpose: Develop AI-based automated CT image analysis tools for detectio...
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Generative-based Airway and Vessel Morphology Quantification on Chest CT Images
Accurately and precisely characterizing the morphology of small pulmonar...
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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) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98 readers, and 9.56 respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.
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