{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:37:57Z","timestamp":1792791477384,"version":"4.5.0"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Computed tomography (CT) provides rich diagnosis and severity information of COVID\u201019 in clinical practice. However, there is no computerized tool to automatically delineate COVID\u201019 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)\u2010based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The DL\u2010based segmentation method employs the \u201cVB\u2010Net\u201d neural network to segment COVID\u201019 infection regions in CT scans. The developed DL\u2010based segmentation system is trained by CT scans from 249 COVID\u201019 patients, and further validated by CT scans from other 300 COVID\u201019 patients. To accelerate the manual delineation of CT scans for training, a human\u2010involved\u2010model\u2010iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL\u2010based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed DL\u2010based segmentation system yielded Dice similarity coefficients of 91.6%\u00a0\u00b1\u00a010.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4\u00a0min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4%\u00a0\u00b1\u00a01.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A DL\u2010based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID\u201019 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14609","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T01:32:08Z","timestamp":1606095128000},"page":"1633-1645","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":180,"title":["Abnormal lung quantification in chest CT images of COVID\u201019 patients with deep learning and its application to severity prediction"],"prefix":"10.1002","volume":"48","author":[{"given":"Fei","family":"Shan","sequence":"first","affiliation":[{"name":"Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai 201508 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaozong","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Research and Development Shanghai United Imaging Intelligence Co., Ltd. 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Shanghai 200232 China"},{"name":"School of Biomedical Engineering ShanghaiTech University Shanghai China"},{"name":"Department of Artificial Intelligence Korea University Seoul 02841 Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai 201508 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001017"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.46234\/ccdcw2020.017"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMc2001272"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001191"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30185-9"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30493-1"},{"key":"e_1_2_7_8_1","first-page":"1","article-title":"A new coronavirus associated with human respiratory disease in China","volume":"579","author":"Wu F","year":"2000","journal-title":"Nature"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"GorbalenyaAE.Severe acute respiratory syndrome\u2010related coronavirus\u2013The species and its viruses a statement of the Coronavirus Study Group. 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