{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:54:06Z","timestamp":1790751246561,"version":"4.1.0"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Contrast Media &amp; Molecular Imaging"],"published-print":{"date-parts":[[2021,9,13]]},"abstract":"<jats:p>Objective. The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods. 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results. The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>P<\/a:mi>\n                        <a:mo>&lt;<\/a:mo>\n                        <a:mn>0.05<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion. The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.<\/jats:p>","DOI":"10.1155\/2021\/2748830","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T17:35:03Z","timestamp":1631727303000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-3837","authenticated-orcid":true,"given":"Xiaoqun","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Pediatric Internal Medicine, Northwest Women\u2019s and Children\u2019s Hospital, Xi\u2019an, Shaanxi 710061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1943-5923","authenticated-orcid":true,"given":"Rong","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Affiliated Hospital of Yan\u2019an University, Yan\u2019an 716000, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8852-3655","authenticated-orcid":true,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Affiliated Hospital of Yan\u2019an University, Yan\u2019an 716000, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.201604-292OC"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019180877"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.prrv.2020.07.001"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1093\/ehjcr\/ytaa415"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1159\/000499108"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-020-01309-5"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.08.008"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0162-z"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103975"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/embc.2019.8857527"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06214-8"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1640835"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/9128527"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2018.2866845"},{"issue":"11","key":"16","first-page":"344","article-title":"Optimization analysis of urban function regional planning based on big data and GIS Technology","volume":"55","author":"X. 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