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The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$0.495\\pm 0.309$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>0.495<\/mml:mn>\n                            <mml:mo>\u00b1<\/mml:mo>\n                            <mml:mn>0.309<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    mm and Dice coefficient of\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$0.985\\pm 0.011$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>0.985<\/mml:mn>\n                            <mml:mo>\u00b1<\/mml:mo>\n                            <mml:mn>0.011<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.\n                  <\/jats:p>","DOI":"10.1038\/s41598-020-80936-4","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T06:03:13Z","timestamp":1610604193000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network"],"prefix":"10.1038","volume":"11","author":[{"given":"Sarah E.","family":"Gerard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Herrmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin T.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Rezoagli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Ippolito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Bellani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Cereda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric A.","family":"Hoffman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W.","family":"Kaczka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph M.","family":"Reinhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"80936_CR1","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","volume":"395","author":"F Zhou","year":"2020","unstructured":"Zhou, F. et al. 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Hoffman and Reinhardt are co-founders and shareholders in VIDA Diagnostics, Inc., a medical imaging software spin-off from the University of Iowa. Dr. Guo is a shareholder in VIDA Diagnostics, Inc. Drs. Kaczka and Herrmann are co-founders and shareholders of OscillaVent, Inc. Dr. Gerard has no competing interests. Yi Xin has no competing interests. Kevin T. Martin has no competing interests. Dr. Rezoagli has no competing interests. Dr. Ippolito has no competing interests. Dr. Bellani has no competing interests. Dr. Cereda has no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1455"}}