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Imag."],"published-print":{"date-parts":[[2022,9,6]]},"abstract":"<jats:p>Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion).Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls.Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 \u00b1 0.07; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 \u00b1 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3\u00a0s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95\u20130.98).Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.<\/jats:p>","DOI":"10.1117\/1.jmi.9.5.054001","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T08:03:03Z","timestamp":1662451383000},"page":"1-19","source":"Crossref","is-referenced-by-count":1,"title":["Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks"],"prefix":"10.1117","volume":"9","author":[{"given":"Aditya","family":"Killekar","sequence":"first","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kajetan","family":"Grodecki","sequence":"additional","affiliation":[{"name":"Medical Univ. of Warsaw"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Lin","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastien","family":"Cadet","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priscilla","family":"McElhinney","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aryabod","family":"Razipour","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cato","family":"Chan","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barry D.","family":"Pressman","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Julien","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Chen","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judit","family":"Simon","sequence":"additional","affiliation":[{"name":"Semmelweis Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pal","family":"Maurovich-Horvat","sequence":"additional","affiliation":[{"name":"Semmelweis Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Gaibazzi","sequence":"additional","affiliation":[{"name":"Azienda Ospedaliero-Univ. di Parma"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Udit","family":"Thakur","sequence":"additional","affiliation":[{"name":"Monash Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"Mancini","sequence":"additional","affiliation":[{"name":"Ctr. 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