{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:44:28Z","timestamp":1711028668732},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09459-7","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T20:02:01Z","timestamp":1708977721000},"page":"7265-7278","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CovLIS-MUnet segmentation model for Covid-19 lung infection regions in CT images"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6124-4410","authenticated-orcid":false,"given":"Manju","family":"Devi","sequence":"first","affiliation":[]},{"given":"Sukhdip","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Shailendra","family":"Tiwari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"issue":"8","key":"9459_CR1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1056\/nejmoa2001017","volume":"382","author":"N Zhu","year":"2020","unstructured":"Zhu N et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8):727\u2013733. https:\/\/doi.org\/10.1056\/nejmoa2001017","journal-title":"N Engl J Med"},{"issue":"3","key":"9459_CR2","first-page":"1","volume":"395","author":"JXY Fang","year":"2020","unstructured":"Fang JXY, Zhang H et al (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 395(3):1\u20132","journal-title":"Radiology"},{"issue":"2","key":"9459_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14358\/PERS.80.2.000","volume":"80","author":"JL Strunk","year":"2020","unstructured":"Strunk JL, Temesgen H, Andersen H, Packalen P (2020) Correlation of chest CTand RTPCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 80(2):1\u20138. https:\/\/doi.org\/10.14358\/PERS.80.2.000","journal-title":"Radiology"},{"issue":"8","key":"9459_CR4","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1056\/nejmp2000929","volume":"382","author":"VJ Munster","year":"2020","unstructured":"Munster VJ, Koopmans M, van Doremalen N, van Riel D, de Wit E (2020) A novel coronavirus emerging in China\u2014key questions for impact assessment. N Engl J Med 382(8):692\u2013694. https:\/\/doi.org\/10.1056\/nejmp2000929","journal-title":"N Engl J Med"},{"key":"9459_CR5","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034","author":"MY Ng","year":"2020","unstructured":"Ng MY et al (2020) Imaging profile of the covid-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging. https:\/\/doi.org\/10.1148\/ryct.2020200034","journal-title":"Radiol Cardiothorac Imaging"},{"issue":"1","key":"9459_CR6","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2020200230","volume":"295","author":"M Chung","year":"2020","unstructured":"Chung M et al (2020) CT imaging features of 2019 novel coronavirus (2019-NCoV). Radiology 295(1):202\u2013207. https:\/\/doi.org\/10.1148\/radiol.2020200230","journal-title":"Radiology"},{"key":"9459_CR7","first-page":"1","volume":"2692","author":"G Ga\u00e1l","year":"2020","unstructured":"Ga\u00e1l G, Maga B, Luk\u00e1cs A (2020) Attention U-net based adversarial architectures for chest X-ray lung segmentation. CEUR Workshop Proc 2692:1\u20137","journal-title":"CEUR Workshop Proc"},{"issue":"1","key":"9459_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-06931-z","volume":"12","author":"AL Aswathy","year":"2022","unstructured":"Aswathy AL, Vinod Chandra SS (2022) Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume. Sci Rep 12(1):1\u201312. https:\/\/doi.org\/10.1038\/s41598-022-06931-z","journal-title":"Sci Rep"},{"key":"9459_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9999368","author":"R Zheng","year":"2021","unstructured":"Zheng R, Zheng Y, Dong-Ye C (2021) Improved 3D U-Net for COVID-19 Chest CT Image Segmentation. Sci Program. https:\/\/doi.org\/10.1155\/2021\/9999368","journal-title":"Sci Program"},{"key":"9459_CR10","doi-asserted-by":"publisher","unstructured":"A. Voulodimos, E. Protopapadakis, I. Katsamenis, A. Doulamis, and N. Doulamis (2021) \u201cDeep learning models for COVID-19 infected area segmentation in CT images,\u201d In: ACM International Conference Proceeding Series, pp. 404\u2013411, doi: https:\/\/doi.org\/10.1145\/3453892.3461322.","DOI":"10.1145\/3453892.3461322"},{"key":"9459_CR11","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.12.20027185","author":"C Zheng","year":"2020","unstructured":"Zheng C et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1101\/2020.03.12.20027185","journal-title":"IEEE Trans Med Imaging"},{"key":"9459_CR12","doi-asserted-by":"publisher","first-page":"100007","DOI":"10.1016\/j.cmpbup.2021.100007","volume":"1","author":"N Saeedizadeh","year":"2021","unstructured":"Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M (2021) COVID TV-Unet: segmenting COVID-19 chest CT images using connectivity imposed Unet. Comput Methods Programs Biomed Update 1:100007. https:\/\/doi.org\/10.1016\/j.cmpbup.2021.100007","journal-title":"Comput Methods Programs Biomed Update"},{"key":"9459_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-022-10237-x","volume":"56","author":"S Minaee","year":"2023","unstructured":"Minaee S, Abdolrashidi A, Su H, Bennamoun M, Zhang D (2023) Biometrics recognition using deep learning: a survey. Artif Intell Rev 56:1\u201349","journal-title":"Artif Intell Rev"},{"issue":"12","key":"9459_CR14","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9459_CR15","doi-asserted-by":"publisher","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV) (pp. 801-818). doi: https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"9459_CR16","doi-asserted-by":"publisher","unstructured":"S. P. Mary, Ankayarkanni, U. Nandini, Sathyabama, and S. Aravindhan (2020) \u201cA Survey on Image Segmentation Using Deep Learning,\u201d J Phys Conf Ser, doi https:\/\/doi.org\/10.1088\/1742-6596\/1712\/1\/012016.","DOI":"10.1088\/1742-6596\/1712\/1\/012016"},{"key":"9459_CR17","doi-asserted-by":"publisher","unstructured":"Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu, (2019) \u201cCCNet: Criss-cross attention for semantic segmentation,\u201d In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 603\u2013612, 2019, doi: https:\/\/doi.org\/10.1109\/ICCV.2019.00069.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"9459_CR18","doi-asserted-by":"publisher","first-page":"16591","DOI":"10.1109\/ACCESS.2021.3053408","volume":"9","author":"TB Olaf Ronneberger","year":"2015","unstructured":"Olaf Ronneberger TB (2015) Philipp Fischer and computer, \u201cU-Net: convolutional networks for biomedical image segmentation.\u201d IEEE Access 9:16591\u201316603. https:\/\/doi.org\/10.1109\/ACCESS.2021.3053408","journal-title":"IEEE Access"},{"issue":"8","key":"9459_CR19","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan DP et al (2020) Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Trans Med Imaging 39(8):2626\u20132637. https:\/\/doi.org\/10.1109\/TMI.2020.2996645","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"9459_CR20","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang G et al (2020) A Noise-Robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging 39(8):2653\u20132663. https:\/\/doi.org\/10.1109\/TMI.2020.3000314","journal-title":"IEEE Trans Med Imaging"},{"key":"9459_CR21","unstructured":"Q. Yan et al., (2020) \u201cCOVID-19 Chest CT image segmentation -- A Deep Convolutional Neural Network Solution,\u201d pp. 1\u201310."},{"issue":"1","key":"9459_CR22","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1002\/ima.22527","volume":"31","author":"T Zhou","year":"2021","unstructured":"Zhou T, Canu S, Ruan S (2021) Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int J Imaging Syst Technol 31(1):16\u201327. https:\/\/doi.org\/10.1002\/ima.22527","journal-title":"Int J Imaging Syst Technol"},{"key":"9459_CR23","doi-asserted-by":"crossref","unstructured":"O. Elharrouss, N. Almaadeed, N. Subramanian, and S. Al-maadeed, (2020) \u201cAn encoder-decoder-based method for COVID-19 lung infection segmentation\u201d.","DOI":"10.29117\/quarfe.2020.0294"},{"key":"9459_CR24","unstructured":"X. Chen, L. Yao, and Y. Zhang, (2020) \u201cResidual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images,\u201d vol. 14, no. 8, pp. 1\u20137"},{"issue":"1","key":"9459_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-020-00529-5","volume":"21","author":"A Saood","year":"2021","unstructured":"Saood A, Hatem I (2021) COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 21(1):1\u201310. https:\/\/doi.org\/10.1186\/s12880-020-00529-5","journal-title":"BMC Med Imaging"},{"key":"9459_CR26","doi-asserted-by":"publisher","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, (2016) \u201cDeep residual learning for image recognition,\u201d In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770\u2013778, doi: https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"9459_CR27","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","volume":"30","author":"YH Wu","year":"2021","unstructured":"Wu YH et al (2021) JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113\u20133126. https:\/\/doi.org\/10.1109\/TIP.2021.3058783","journal-title":"IEEE Trans Image Process"},{"key":"9459_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106402","author":"R Xu","year":"2023","unstructured":"Xu R, Wang C, Xu S, Meng W, Zhang X (2023) Dual-stream representation fusion learning for accurate medical image segmentation. Eng Appl Artif Intell. https:\/\/doi.org\/10.1016\/j.engappai.2023.106402","journal-title":"Eng Appl Artif Intell"},{"key":"9459_CR29","unstructured":"R. , W. C. , X. S. , M. W. , Z. X. Xu, DC-Net: Dual Context Network for 2D Medical Image Segmentation. , 1st ed., vol. 12901. Springer Cham, 2021."},{"key":"9459_CR30","doi-asserted-by":"publisher","DOI":"10.36079\/lamintang.ijai-0902.405","author":"F Sadeghi","year":"2022","unstructured":"Sadeghi F, Taheri M, Rastgarpour M, Sharifi A (2022) A novel sep-unet architecture of convolutional neural networks to improve dermoscopic image segmentation by training parameters reduction. Int J Artif Intell. https:\/\/doi.org\/10.36079\/lamintang.ijai-0902.405","journal-title":"Int J Artif Intell"},{"key":"9459_CR31","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2022.021206","author":"R Rajaragavi","year":"2022","unstructured":"Rajaragavi R, Palanivel Rajan S (2022) Optimized U-net segmentation and hybrid res-net for brain tumor MRI images classification. Intell Autom Soft Comput. https:\/\/doi.org\/10.32604\/iasc.2022.021206","journal-title":"Intell Autom Soft Comput"},{"key":"9459_CR32","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1499","author":"T Yousefi","year":"2023","unstructured":"Yousefi T, Akta\u015f \u00d6 (2023) New hybrid segmentation algorithm: UNet-GOA. PeerJ Comput Sci. https:\/\/doi.org\/10.7717\/peerj-cs.1499","journal-title":"PeerJ Comput Sci"},{"key":"9459_CR33","unstructured":"Z. Tang et al., \u201cSeverity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images,\u201d vol. 2019, pp. 1\u201318, 2020."},{"issue":"6","key":"9459_CR34","doi-asserted-by":"publisher","first-page":"065031","DOI":"10.1088\/1361-6560\/abe838","volume":"66","author":"F Shi","year":"2021","unstructured":"Shi F, Xia L, Shan F, Song B, Wu D, Wei Y, Yuan H, Jiang H, He Y, Gao Y, Sui H (2021) Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Phys Med Biol 66(6):065031","journal-title":"Phys Med Biol"},{"issue":"8","key":"9459_CR35","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1007\/s00330-020-06801-0","volume":"30","author":"Z Ye","year":"2020","unstructured":"Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol 30(8):4381\u20134389. https:\/\/doi.org\/10.1007\/s00330-020-06801-0","journal-title":"Eur Radiol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09459-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09459-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09459-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:20:32Z","timestamp":1711027232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09459-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":35,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9459"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09459-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"13 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","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 have no conflicts of interest, financial or otherwise.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}