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These studies show that non-contrast chest CT has been useful to detect, quantify severity, and assess progression of the disease. The primary features observed on a lung affected by COVID-19 are peripheral focal or multi-focal ground glass opacities, consolidation, and crazy-paving patterns.
Materials and Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in ...
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 ...
Materials and Methods: In this retrospective study, the proposed method takes as input a noncontrast chest CT and segments the lesions, lungs, and lobes in ...
Apr 2, 2020 · Materials and Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs ...
Sep 13, 2024 · Conclusion: A new method identifies regions of abnormalities seen in COVID-19 non-contrasted Chest CT and computes (POO, POHO) and (LSS, LHOS) ...
Jul 29, 2020 · This study proposes measures of disease severity and a deep learning and deep reinforcement–based method to compute them.
Oct 5, 2023 · This is the first study to investigate the usefulness of AI-based CT quantification of COVID-19 pneumonia for predicting critical outcomes and ...
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Jan 11, 2021 · Previous studies revealed the chest CT patterns of COVID-19 with a typical change from early subpleural ground-glass opacity (GGO) to extensive ...
Apr 20, 2020 · Materials and methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs ...