{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:11:20Z","timestamp":1775578280218,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-022-00209-4","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T04:39:07Z","timestamp":1675053547000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation"],"prefix":"10.1007","volume":"11","author":[{"given":"Haonan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Peng","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Osmar","family":"Zaiane","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"209_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60\u201388.","journal-title":"Med Image Anal."},{"issue":"1","key":"209_CR2","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1146\/annurev.bioeng.2.1.315","volume":"2","author":"DL Pham","year":"2000","unstructured":"Pham DL, Chenyang X, Prince JL. Current methods in medical image segmentation. Ann Rev Biomed Eng. 2000;2(1):315\u201337.","journal-title":"Ann Rev Biomed Eng."},{"issue":"6","key":"209_CR3","first-page":"945","volume":"29","author":"W Tan","year":"2021","unstructured":"Tan W, Huang P, Li X, Ren G, Chen Y, Yang J. Analysis of segmentation of lung parenchyma based on deep learning methods. J X-Ray Sci Technol. 2021;29(6):945\u201359.","journal-title":"J X-Ray Sci Technol."},{"issue":"5","key":"209_CR4","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1049\/ipr2.12423","volume":"16","author":"W Tan","year":"2022","unstructured":"Tan W, Liu P, Li X, Shaoxun X, Chen Y, Yang J. Segmentation of lung airways based on deep learning methods. IET Image Process. 2022;16(5):1444\u201356.","journal-title":"IET Image Process."},{"key":"209_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107810","volume":"112","author":"L Wang","year":"2021","unstructured":"Wang L, Juan G, Chen Y, Liang Y, Zhang W, Jiantao P, Chen H. Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network. Pattern Recognit. 2021;112: 107810.","journal-title":"Pattern Recognit."},{"key":"209_CR6","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, 2015;234\u2013241. Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"209_CR7","doi-asserted-by":"crossref","unstructured":"Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH. Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science, pp 287\u2013297, Cham, 2018.","DOI":"10.1007\/978-3-319-75238-9_25"},{"issue":"1","key":"209_CR8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk T, Mai D, Bensch R, Ronneberger O. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1):67\u201370.","journal-title":"Nat Methods"},{"key":"209_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107756","volume":"113","author":"Y Qian","year":"2021","unstructured":"Qian Y, Gao Y, Zheng Y, Zhu J, Dai Y, Shi Y. Crossover-Net: leveraging vertical-horizontal crossover relation for robust medical image segmentation. Pattern Recognit. 2021;113: 107756.","journal-title":"Pattern Recognit."},{"issue":"6","key":"209_CR10","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Md Zongwei Zhou","year":"2020","unstructured":"Zongwei Zhou Md, Siddiquee MR, Tajbakhsh N, Liang J. UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging. 2020;39(6):1856\u201367.","journal-title":"IEEE Trans Med Imaging"},{"key":"209_CR11","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Sohel RM. MultiResUNet\u202f: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 2020;121:74\u201387.","journal-title":"Neural Netw."},{"key":"209_CR12","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. In: Advances in Neural Information Processing Systems, volume\u00a030. Curran Associates, Inc., 2017."},{"key":"209_CR13","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D. Attention U-Net: Learning Where to Look for the Pancreas. arXiv:1804.03999 [cs], 2018."},{"key":"209_CR14","unstructured":"ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI."},{"issue":"12","key":"209_CR15","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Chen H, Qi X, Dou Q, Chi-Wing F, Heng P-A. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation From CT volumes. IEEE Trans Med Imaging. 2018;37(12):2663\u201374.","journal-title":"IEEE Trans Med Imaging"},{"key":"209_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778, Las Vegas, NV, USA, 2016. IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"209_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101957","volume":"92","author":"J Yang","year":"2021","unstructured":"Yang J, Bo W, Li L, Cao P, Zaiane O. MSDS-UNet: a multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT. Comput Med Imaging Graph. 2021;92: 101957.","journal-title":"Comput Med Imaging Graph."},{"key":"209_CR18","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61\u201378.","journal-title":"Med Image Anal."},{"issue":"5","key":"209_CR19","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TMI.2019.2951844","volume":"39","author":"X Li","year":"2020","unstructured":"Li X, Xiaowei H, Lequan Y, Zhu L, Chi-Wing F, Heng P-A. CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging. 2020;39(5):1483\u201393.","journal-title":"IEEE Trans Med Imaging"},{"key":"209_CR20","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: efficient channel attention for deep convolutional neural networks. p\u00a09."},{"key":"209_CR21","unstructured":"Lee C-Y, Xie S, Gallagher P, Zhang Z, Tu Z. Deeply-supervised nets. In: Guy Lebanon and S.\u00a0V.\u00a0N. Vishwanathan, (eds), In: Proceedings of the eighteenth international conference on artificial intelligence and statistics, volume\u00a038 of Proceedings of Machine Learning Research, pp 562\u2013570, San Diego, 2015. PMLR."},{"issue":"3","key":"209_CR22","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 2018;3(3):25.","journal-title":"Data"},{"issue":"02","key":"209_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.6.2.025008","volume":"6","author":"C Kou","year":"2019","unstructured":"Kou C, Li W, Liang W, Zekuan Y, Hao J. Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J Med Imaging. 2019;6(02):1.","journal-title":"J Med Imaging"},{"key":"209_CR24","doi-asserted-by":"crossref","unstructured":"Zhou Y, He X, Huang L,\u00a0Liu L, Zhu F, Cui S, Shao L. Collaborative learning of semi-supervised segmentation and classification for medical images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2079\u20132088, 2019.","DOI":"10.1109\/CVPR.2019.00218"},{"issue":"4","key":"209_CR25","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2018;40(4):834\u201348.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"209_CR26","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/TMI.2020.3025308","volume":"40","author":"Y Xie","year":"2021","unstructured":"Xie Y, Zhang J, Hao L, Shen C, Xia Y. SESV: accurate medical image segmentation by predicting and correcting errors. IEEE Trans Med Imaging. 2021;40(1):286\u201396.","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00209-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-022-00209-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00209-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:20:11Z","timestamp":1744212011000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-022-00209-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["209"],"URL":"https:\/\/doi.org\/10.1007\/s13755-022-00209-4","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]},"assertion":[{"value":"17 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"10"}}