{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T10:44:24Z","timestamp":1771929864171,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"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":[[2023,8]]},"DOI":"10.1007\/s00521-021-06636-w","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T07:02:30Z","timestamp":1635231750000},"page":"16143-16155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions"],"prefix":"10.1007","volume":"35","author":[{"given":"Abdul","family":"Qayyum","sequence":"first","affiliation":[]},{"given":"Mona","family":"Mazhar","sequence":"additional","affiliation":[]},{"given":"Imran","family":"Razzak","sequence":"additional","affiliation":[]},{"given":"Mohamed Reda","family":"Bouadjenek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"6636_CR1","doi-asserted-by":"crossref","unstructured":"Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: a capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint http:\/\/arxiv.org\/abs\/2004.02696","DOI":"10.3389\/frai.2021.598932"},{"key":"6636_CR2","doi-asserted-by":"publisher","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar A, Modzelewski R, Li H, Ruan S (2020) Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: classification and segmentation. Comput Biol Med 126:104037","journal-title":"Comput Biol Med"},{"key":"6636_CR3","unstructured":"Bizopoulos P, Vretos N, Daras P (2020) Comprehensive comparison of deep learning models for lung and covid-19 lesion segmentation in ct scans. arXiv preprint http:\/\/arxiv.org\/abs\/2009.06412"},{"key":"6636_CR4","doi-asserted-by":"crossref","unstructured":"Born J, Wiedemann N, Br\u00a8andle G, Buhre C, Rieck B, Borgwardt K (2020) Accelerating covid-19 differential diagnosis with explainable ultrasound image analysis. arXiv preprint http:\/\/arxiv.org\/abs\/2009.06116","DOI":"10.1136\/thorax-2020-BTSabstracts.404"},{"key":"6636_CR5","doi-asserted-by":"crossref","unstructured":"Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, Hu S, Wang Y, Hu X, Zheng B et al (2020) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high- resolution computed tomography: a prospective study. MedRxiv","DOI":"10.1101\/2020.02.25.20021568"},{"key":"6636_CR6","unstructured":"Chen P, Xu C, Li X, Ma Y, Sun F (2019) Two-stage network for oar segmentation. In: SegTHOR@ ISBI"},{"key":"6636_CR7","doi-asserted-by":"crossref","unstructured":"Elharrouss O, Subramanian N, Al-Maadeed S (2020) An encoder-decoder-based method for covid-19 lung infection segmentation. arXiv preprint http:\/\/arxiv.org\/abs\/2007.00861","DOI":"10.29117\/quarfe.2020.0294"},{"issue":"8","key":"6636_CR8","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shen J, Shao L (2020) Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging 39(8):2626\u20132637","journal-title":"IEEE Trans Med Imaging"},{"key":"6636_CR9","unstructured":"Han M, Yao G, Zhang W, Mu G, Zhan Y, Zhou X, Gao Y (2019) Segmen- tation of ct thoracic organs by multi-resolution vb-nets. In: SegTHOR@ ISBI"},{"key":"6636_CR10","doi-asserted-by":"publisher","first-page":"103738","DOI":"10.1016\/j.compbiomed.2020.103738","volume":"120","author":"MK Hasan","year":"2020","unstructured":"Hasan MK, Dahal L, Samarakoon PN, Tushar FI, Robert M (2020) Dsnet: auto- matic dermoscopic skin lesion segmentation. Comput Biol Med 120:103738","journal-title":"Comput Biol Med"},{"key":"6636_CR11","doi-asserted-by":"publisher","first-page":"101666","DOI":"10.1016\/j.media.2020.101666","volume":"61","author":"T He","year":"2020","unstructured":"He T, Hu J, Song Y, Guo J, Yi Z (2020) Multi-task learning for the segmentation of organs at risk with label dependence. Med Image Anal 61:101666","journal-title":"Med Image Anal"},{"key":"6636_CR12","unstructured":"Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with em routing. In: International confer- ence on learning representations"},{"key":"6636_CR13","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6636_CR14","doi-asserted-by":"crossref","unstructured":"Kausar A, Razzak I, Shapiai MI, Beheshti A (2021) 3d shallow deep neural network for fast and precise segmentation of left atrium. Multimed Syst 1\u201311","DOI":"10.1109\/IJCNN52387.2021.9534418"},{"key":"6636_CR15","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, et al (2020) Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology"},{"issue":"22","key":"6636_CR16","doi-asserted-by":"publisher","first-page":"225034","DOI":"10.1088\/1361-6560\/abc04e","volume":"65","author":"J Ma","year":"2020","unstructured":"Ma J, Nie Z, Wang C, Dong G, Zhu Q, He J, Gui L, Yang X (2020) Active contour regularized semi-supervised learning for covid-19 ct infection segmentation with limited annotations. Phys Med Biol 65(22):225034","journal-title":"Phys Med Biol"},{"key":"6636_CR17","unstructured":"Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, Zhu Q, Dong G, He J, He Z et al (2020) Towards efficient covid-19 ct annotation: a benchmark for lung and infection segmentation. arXiv preprint http:\/\/arxiv.org\/abs\/2004.12537"},{"key":"6636_CR18","doi-asserted-by":"crossref","unstructured":"Naseem U, Razzak I, Khushi M, Eklund PW, Kim J (2021) Covidsenti: a large-scale benchmark twitter data set for covid-19 sentiment analysis. IEEE Trans Comput Soc Syst","DOI":"10.1109\/TCSS.2021.3051189"},{"key":"6636_CR19","doi-asserted-by":"publisher","first-page":"107747","DOI":"10.1016\/j.patcog.2020.107747","volume":"114","author":"A Oulefki","year":"2020","unstructured":"Oulefki A, Agaian S, Trongtirakul T, Laouar AK (2020) Automatic covid-19 lung infected region segmentation and measurement using ct-scans images. Pattern Recognit 114:107747","journal-title":"Pattern Recognit"},{"key":"6636_CR20","doi-asserted-by":"crossref","unstructured":"Qayyum A, Razzak I, Tanveer M, Kumar A (2021) Depth-wise dense neural network for automatic covid19 infection detection and diagnosis. Ann Oper Res 1\u201321","DOI":"10.1007\/s10479-021-04154-5"},{"issue":"5","key":"6636_CR21","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1109\/JBHI.2018.2874033","volume":"23","author":"MI Razzak","year":"2018","unstructured":"Razzak MI, Imran M, Xu G (2018) Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inf 23(5):1911\u20131919","journal-title":"IEEE J Biomed Health Inf"},{"issue":"9","key":"6636_CR22","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.1007\/s00521-019-04095-y","volume":"32","author":"MI Razzak","year":"2020","unstructured":"Razzak MI, Imran M, Xu G (2020) Big data analytics for preventive medicine. Neural Comput Appl 32(9):4417\u20134451","journal-title":"Neural Comput Appl"},{"key":"6636_CR23","doi-asserted-by":"crossref","unstructured":"Rehman A, Naz S, Khan A, Zaib A, Razzak I (2020) Improving coronavirus (covid-19) diagnosis using deep transfer learning. MedRxiv","DOI":"10.1101\/2020.04.11.20054643"},{"key":"6636_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image seg- mentation. In: International conference on medical image computing and computer-assisted intervention, Springer. pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6636_CR25","doi-asserted-by":"crossref","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excita- tion\u2019in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention, Springer. pp 421\u2013429","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"6636_CR26","doi-asserted-by":"crossref","unstructured":"Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M (2020) Covid tv-unet: segment- ing covid-19 chest ct images using connectivity imposed u-net. arXiv preprint http:\/\/arxiv.org\/abs\/2007.12303","DOI":"10.1016\/j.cmpbup.2021.100007"},{"issue":"1","key":"6636_CR27","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","journal-title":"BMC Med Imaging"},{"key":"6636_CR28","unstructured":"Vesal S, Ravikumar N, Maier A (2019) A 2d dilated residual u-net for multi-organ segmenta- tion in thoracic ct. arXiv preprint http:\/\/arxiv.org\/abs\/1905.07710"},{"issue":"8","key":"6636_CR29","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang G, Liu X, Li C, Zhiyong Xu, Ruan J, Zhu H, Meng T, Li K, Huang N, Zhang S (2020) A noise-robust framework for automatic segmentation of covid-19 pneumonia le- sions from ct images. IEEE Trans Med Imaging 39(8):2653\u20132663","journal-title":"IEEE Trans Med Imaging"},{"key":"6636_CR30","first-page":"1","volume":"3","author":"Q Wang","year":"2019","unstructured":"Wang Q, Zhao W, Zhang CH, Zhang L, Wang C, Li Z, Cui S, Li G (2019) 3d enhanced multi-scale network for thoracic organs segmentation. SegTHOR@ ISBI 3:1\u20135","journal-title":"SegTHOR@ ISBI"},{"issue":"8","key":"6636_CR31","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TMI.2020.2995108","volume":"39","author":"W Xie","year":"2020","unstructured":"Xie W, Jacobs C, Charbonnier JP, van Ginneken B (2020) Relational modeling for robust and efficient pulmonary lobe segmentation in ct scans. IEEE Trans Med Imaging 39(8):2664\u20132675","journal-title":"IEEE Trans Med Imaging"},{"key":"6636_CR32","unstructured":"Xu Z, Cao Y, Jin C, Shao G, Liu X, Zhou J, Shi H, Feng J (2020) Gasnet: weakly-supervised framework for covid-19 lesion segmentation. arXiv preprint http:\/\/arxiv.org\/abs\/2010.09456"},{"key":"6636_CR33","doi-asserted-by":"publisher","first-page":"185786","DOI":"10.1109\/ACCESS.2020.3027738","volume":"8","author":"B Zheng","year":"2020","unstructured":"Zheng B, Liu Y, Zhu Y, Yu F, Jiang T, Yang D, Xu T (2020) Msd-net: multi-scale discriminative network for covid-19 lung infection segmentation on ct. IEEE Access 8:185786\u2013185795","journal-title":"IEEE Access"},{"key":"6636_CR34","unstructured":"Zhou T, Canu S, Ruan S (2020) An automatic covid-19 ct segmentation network using spatial and channel attention mechanism. arXiv preprint http:\/\/arxiv.org\/abs\/2004.06673"},{"issue":"1","key":"6636_CR35","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","journal-title":"Int J Imaging Syst Technol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06636-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06636-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06636-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T20:48:02Z","timestamp":1689194882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06636-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":35,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["6636"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06636-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"26 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}