{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T05:43:04Z","timestamp":1774590184637,"version":"3.50.1"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004295","name":"Shandong University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004295","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,6,19]]},"abstract":"<jats:p>Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. The rapid and accurate automatic segmentation of COVID-19 infected areas using chest computed tomography (CT) scans is critical for assessing disease progression. However, infected areas have irregular sizes and shapes. Furthermore, there are large differences between image features. We propose a convolutional neural network, named 3D CU-Net, to automatically identify COVID-19 infected areas from 3D chest CT images by extracting rich features and fusing multiscale global information. 3D CU-Net is based on the architecture of 3D U-Net. We propose an attention mechanism for 3D CU-Net to achieve local cross-channel information interaction in an encoder to enhance different levels of the feature representation. At the end of the encoder, we design a pyramid fusion module with expanded convolutions to fuse multiscale context information from high-level features. The Tversky loss is used to resolve the problems of the irregular size and uneven distribution of lesions. Experimental results show that 3D CU-Net achieves excellent segmentation performance, with Dice similarity coefficients of 96.3% and 77.8% in the lung and COVID-19 infected areas, respectively. 3D CU-Net has high potential to be used for diagnosing COVID-19.<\/jats:p>","DOI":"10.1155\/2021\/9999368","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:20:05Z","timestamp":1624666805000},"page":"1-9","source":"Crossref","is-referenced-by-count":20,"title":["Improved 3D U-Net for COVID-19 Chest CT Image Segmentation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1357-5298","authenticated-orcid":true,"given":"Ruiyong","family":"Zheng","sequence":"first","affiliation":[{"name":"Collage of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1328-5485","authenticated-orcid":true,"given":"Yongguo","family":"Zheng","sequence":"additional","affiliation":[{"name":"Collage of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4018-293X","authenticated-orcid":true,"given":"Changlei","family":"Dong-Ye","sequence":"additional","affiliation":[{"name":"Collage of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25678"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(20)30185-9"},{"key":"3","volume-title":"WHO Director-General\u2019s Opening Remarks at the media Briefing on COVID-19 11 March 2020","author":"World Health Organization (WHO)","year":"2020"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200432"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200642"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200343"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa2001017"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200236"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200079"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2996645"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101836"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3054746"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2021.3056564"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"18","article-title":"Attention u-net: Learning where to look for the pancreas","author":"O. Oktay","year":"2018"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/ITME.2018.00080"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2983721"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"22","article-title":"Covid-19 ct lung and infection segmentation dataset [DB\/OL]. Zenodo","author":"M. Jun","year":"2020"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.17816\/dd46826"},{"key":"24","article-title":"Towards efficient covid-19 ct annotation: a benchmark for lung and infection segmentation","author":"J. Ma","year":"2020"},{"key":"25","article-title":"Automated chest CT image segmentation of COVID-19 lung infection based on 3D U-Net","author":"D. M\u00fcller","year":"2020"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/9999368.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/9999368.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/9999368.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:20:09Z","timestamp":1624666809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2021\/9999368\/"}},"subtitle":[],"editor":[{"given":"Shah","family":"Nazir","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,6,19]]},"references-count":25,"alternative-id":["9999368","9999368"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9999368","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"value":"1875-919X","type":"electronic"},{"value":"1058-9244","type":"print"}],"subject":[],"published":{"date-parts":[[2021,6,19]]}}}