{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:29:01Z","timestamp":1782948541452,"version":"3.54.5"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CZY22015"],"award-info":[{"award-number":["CZY22015"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Lung cancer is one of the deadliest cancers in the world and is a serious threat to human life. Lung nodules are an early manifestation of lung cancer, early detection and treatment of which can improve the survival rate of patients. In order to accurately segment the lung nodule regions in lung CT images, CA\u2010UNet, an encoding and decoding structure based on convolution and attention fusion, is proposed based on the U\u2010Net network. It has improved on two points: First, at the skip connection, the global feature information is extracted using the Swin Transformer block and then fused with the pre\u2010extraction features and subsequently fed into the corresponding layer of the decoder; second, each channel information is reweighted in the decoder by the channel attention module so that the network focuses on more important channels. Experimental results on the LIDC\u2010IDRI public database of lung nodules showed that the intersection of union, dice similarity coefficient, precision, and recall of the algorithm were 82.42%, 89.86%, 89.07%, and 92.44%, respectively. The algorithm has better segmentation performance compared to other segmentation methods.<\/jats:p>","DOI":"10.1002\/ima.22878","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T05:23:55Z","timestamp":1679030635000},"page":"1469-1479","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["<scp>CA\u2010UNet<\/scp>\n                    : Convolution and attention fusion for lung nodule segmentation"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4732-040X","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology South\u2010Central Minzu University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fubin","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology South\u2010Central Minzu University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology South\u2010Central Minzu University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengzhou","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology South\u2010Central Minzu University  Wuhan China"},{"name":"Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3788\/OPE.20182605.1211"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13939"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/crj.13084"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-020-00346-w"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/21642583.2022.2045645"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.32604\/biocell.2023.025905"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"LongJ ShelhamerE DarrellT.Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. pp. 3431\u20103440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_9_9_1","unstructured":"KrizhevskyA SutskeverI HintonGEJ.A INIPS imagenet classification with deep convolutional neural networks; 2012 p. 25."},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer\u2010Assisted Intervention; 2015 pp. 234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3003914"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"BaiW SuzukiH QinC et al.Recurrent neural networks for aortic image sequence segmentation with sparse annotations. International Conference on Medical Image Computing and Computer\u2010Assisted Intervention; 2018. pp. 586\u2010594.","DOI":"10.1007\/978-3-030-00937-3_67"},{"issue":"17","key":"e_1_2_9_13_1","first-page":"203","article-title":"Improved U\u2010NET network for lung nodule segmentation","volume":"56","author":"Zhong S","year":"2020","journal-title":"Optik"},{"key":"e_1_2_9_14_1","first-page":"3","article-title":"Unet++: A nested U\u2010net architecture for medical image segmentation, deep learning in medical image analysis and multimodal learning for clinical decision support","volume":"11045","author":"Zhou Z","year":"2018","journal-title":"Springer"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11101614"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/15330338221124372"},{"key":"e_1_2_9_17_1","unstructured":"DosovitskiyA BeyerL KolesnikovA et al.An image is worth 16 \u00d7\u200916 words: transformers for image recognition at scale.2020."},{"key":"e_1_2_9_18_1","unstructured":"VaswaniA ShazeerN ParmarN et al.Attention is all you need; 2017. p. 30."},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"PanX GeC LuR et al.On the integration of self\u2010attention and convolution. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022. pp. 805\u2010815.","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"HuJ ShenL SunG.Squeeze\u2010and\u2010excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 7132\u20107141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"WooS ParkJ LeeJ\u2010Y et al.Cbam: convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV) 2018. pp. 3\u201019.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_9_22_1","unstructured":"ChenJ LuY YuQ et al.Transunet: transformers make strong encoders for medical image segmentation.2021."},{"key":"e_1_2_9_23_1","unstructured":"CaoH WangY ChenJ et al.Swin\u2010Unet: Unet\u2010like pure transformer for medical image segmentation.2021."},{"key":"e_1_2_9_24_1","doi-asserted-by":"crossref","unstructured":"LiuZ LinY CaoY et al.Swin Transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2021. pp. 10012\u201010022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_2_9_25_1","unstructured":"IoffeS SzegedyC.Batch normalization: accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning 2015. pp. 448\u2010456."},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS et al.Delving deep into rectifiers: surpassing human\u2010level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision 2015. pp. 1026\u20101034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"e_1_2_9_28_1","doi-asserted-by":"crossref","unstructured":"SelvarajuRR CogswellM DasA et al.Grad\u2010cam: Visual explanations from deep networks via gradient\u2010based localization. Proceedings of the IEEE International Conference on Computer Vision; 2017. pp. 618\u2010626.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22878","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ima.22878","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T04:28:14Z","timestamp":1780288094000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.22878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":27,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["10.1002\/ima.22878"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/ima.22878","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,17]]},"assertion":[{"value":"2022-11-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}