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      COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images


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          We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non‐square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID‐19 lesions segmentation was evaluated on an external reverse transcription‐polymerase chain reaction positive COVID‐19 dataset (7′333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98–0.99) and 0.91 ± 0.038 (95% CI, 0.90–0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, −0.12 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16–0.59) and 0.81 ± 6.6% (95% CI, −0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first‐order feature (−6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL‐guided three‐dimensional whole lung and infected regions segmentation in COVID‐19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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              U-Net: Convolutional Networks for Biomedical Image Segmentation


                Author and article information

                Int J Imaging Syst Technol
                Int J Imaging Syst Technol
                International Journal of Imaging Systems and Technology
                John Wiley & Sons, Inc. (Hoboken, USA )
                28 October 2021
                28 October 2021
                : 10.1002/ima.22672
                [ 1 ] Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva Switzerland
                [ 2 ] Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran
                [ 3 ] Department of Radiology Technology Shahid Beheshti University of Medical Sciences Tehran Iran
                [ 4 ] Research and Development Department Med Fanavaran Plus Co. Karaj Iran
                [ 5 ] Clinical Research Development Center Qom University of Medical Sciences Qom Iran
                [ 6 ] Men’s Health and Reproductive Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran
                [ 7 ] Department of Radiologic Technology, Faculty of Allied Medicine Kerman University of Medical Sciences Kerman Iran
                [ 8 ] Department of Radiology Shariati Hospital, Tehran University of Medical Sciences Tehran Iran
                [ 9 ] Department of Cardiovascular Sciences KU Leuven Leuven Belgium
                [ 10 ] Geneva University Neurocenter Geneva University Geneva Switzerland
                [ 11 ] Department of Nuclear Medicine and Molecular Imaging University of Groningen, University Medical Center Groningen Groningen Netherlands
                [ 12 ] Department of Nuclear Medicine University of Southern Denmark Odense Denmark
                Author notes
                [*] [* ] Correspondence

                Habib Zaidi, Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‐1211 Geneva, Switzerland.

                Email: habib.zaidi@ 123456hcuge.ch

                Author information
                © 2021 The Authors. International Journal of Imaging Systems and Technology published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                : 18 September 2021
                : 10 June 2021
                : 17 October 2021
                Page count
                Figures: 7, Tables: 3, Pages: 14, Words: 7412
                Funded by: Swiss National Science Foundation , doi 10.13039/501100001711;
                Award ID: SNRF 320030_176052
                Funded by: WOA Institution: Universite de Geneve
                Funded by: Blended DEAL: CSAL
                Research Article
                Research Articles
                Custom metadata
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.9 mode:remove_FC converted:08.12.2021

                covid‐19,deep learning,pneumonia,segmentation,x‐ray ct
                covid‐19, deep learning, pneumonia, segmentation, x‐ray ct


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