{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:40:47Z","timestamp":1781602847405,"version":"3.54.5"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZY21F030002"],"award-info":[{"award-number":["LZY21F030002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00521-021-06684-2","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:03:47Z","timestamp":1641513827000},"page":"16051-16059","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image"],"prefix":"10.1007","volume":"35","author":[{"given":"Haihua","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luya","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanchen","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-9684","authenticated-orcid":false,"given":"Honghao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"issue":"40","key":"6684_CR1","first-page":"941","volume":"9","author":"R Krithiga","year":"2016","unstructured":"Krithiga R (2016) A survey: segmentation in dental X-ray images for diagnosis of dental caries. Int J Control Theory Appl 9(40):941","journal-title":"Int J Control Theory Appl"},{"issue":"3","key":"6684_CR2","first-page":"161","volume":"21","author":"HX Lu","year":"2018","unstructured":"Lu HX, Tao DY, Lo ECM, Li R, Wang X, Tai BJ, Hu Y, Lin HC, Wang B, Si Y et al (2018) The 4th national oral health survey in the mainland of China: background and methodology. Chin J Dent Res 21(3):161","journal-title":"Chin J Dent Res"},{"issue":"10159","key":"6684_CR3","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","volume":"392","author":"SL James","year":"2018","unstructured":"James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A et al (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories. The Lancet 392(10159):1789","journal-title":"The Lancet"},{"issue":"9","key":"6684_CR4","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.3390\/diagnostics11091672","volume":"11","author":"L Lian","year":"2021","unstructured":"Lian L, Zhu T, Zhu F, Zhu H (2021) Deep learning for caries detection and classification. Diagnostics 11(9):1672","journal-title":"Diagnostics"},{"key":"6684_CR5","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) Squeeze-and-excitation networks, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer), pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"5","key":"6684_CR6","first-page":"250","volume":"7","author":"AA ALbahbah","year":"2016","unstructured":"ALbahbah AA, El-Bakry HM, Abd-Elgahany S (2016) Detection of caries in panoramic dental X-ray images using back-propagation neural network. Int J Electron Commun Comput Eng 7(5):250","journal-title":"Int J Electron Commun Comput Eng"},{"key":"6684_CR7","unstructured":"Ali R.B, Ejbali R, Zaied M (2016) Detection and classification of dental caries in x-ray images using deep neural networks, in International Conference on Software Engineering Advances (ICSEA) , p. 236"},{"key":"6684_CR8","doi-asserted-by":"crossref","unstructured":"Sornam M, Prabhakaran M (2017) In 2017 IEEE Int Conf Power. Signals and Instrumentation Engineering (ICPCSI) (IEEE, Control, pp 2698\u20132703","DOI":"10.1109\/ICPCSI.2017.8392208"},{"key":"6684_CR9","doi-asserted-by":"crossref","unstructured":"Prajapati S.A, Nagaraj R, Mitra S (2017) Classification of dental diseases using CNN and transfer learning, in 2017 5th International Symposium on Computational and Business Intelligence (ISCBI) (IEEE), pp. 70\u201374","DOI":"10.1109\/ISCBI.2017.8053547"},{"issue":"002","key":"6684_CR10","first-page":"103","volume":"52","author":"Z Zhiling","year":"2017","unstructured":"Zhiling Z (2017) Study on the sensitivity of computer-aided detection of adjacent caries in cone-beam CT images. Chinese J Stomatol 52(002):103","journal-title":"Chinese J Stomatol"},{"issue":"09","key":"6684_CR11","first-page":"654","volume":"55","author":"L Xiujiao","year":"2020","unstructured":"Xiujiao L, Dong Z, Minyi H, Hui C, Hao Y (2020) Study on the sensitivity of computer-aided detection of adjacent caries in cone-beam CT images. Chinese J Stomatol 55(09):654","journal-title":"Chinese J Stomatol"},{"key":"6684_CR12","unstructured":"Haghanifar A, Majdabadi M.M, Ko, S.B (2020) Paxnet: Dental caries detection in panoramic x-ray using ensemble transfer learning and capsule classifier, arXiv preprint arXiv:2012.13666"},{"issue":"2","key":"6684_CR13","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","volume":"7","author":"Y Guo","year":"2018","unstructured":"Guo Y, Liu Y, Georgiou T, Lew MS (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inform Retriev 7(2):87","journal-title":"Int J Multimed Inform Retriev"},{"key":"6684_CR14","doi-asserted-by":"crossref","unstructured":"Corbella S, Srinivas S, Cabitza F (2020) Applications of deep learning in dentistry, Oral Surgery. Oral Med, Oral Pathol Oral Radiol","DOI":"10.1016\/j.oooo.2020.11.003"},{"key":"6684_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks, Adv Neural Inform Process Syst pp. 1097\u20131105"},{"key":"6684_CR16","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"6684_CR17","doi-asserted-by":"crossref","unstructured":"Li Y, Qi H, Dai J, Ji X, Wei Y (2017) Fully convolutional instance-aware semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 2359\u20132367","DOI":"10.1109\/CVPR.2017.472"},{"issue":"1","key":"6684_CR18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TIP.2017.2754941","volume":"27","author":"W Wang","year":"2017","unstructured":"Wang W, Shen J, Shao L (2017) Video salient object detection via fully convolutional networks. IEEE Trans Image Process 27(1):38","journal-title":"IEEE Trans Image Process"},{"key":"6684_CR19","doi-asserted-by":"crossref","unstructured":"Caelles S, Maninis KK, Pont-Tuset J, Leal-Taix\u00e9 L, Cremers D, Van Gool L (2017) One-shot video object segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 221\u2013230","DOI":"10.1109\/CVPR.2017.565"},{"key":"6684_CR20","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.patrec.2020.12.009","volume":"142","author":"Z Cao","year":"2021","unstructured":"Cao Z, Sun C, Wang W, Zheng X, Wu J, Gao H (2021) Multi-modality fusion learning for the automatic diagnosis of optic neuropathy. Patt Recognit Lett 142:58","journal-title":"Patt Recognit Lett"},{"issue":"12","key":"6684_CR21","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) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Patt Anal Mach Intell 39(12):2481","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"6684_CR22","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp S.S, Brox T, Ronneberger O (2016) 3D U-Net: Learning dense volumetric segmentation from sparse annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer), pp. 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"6684_CR23","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S.A (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV) (IEEE), pp. 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"6684_CR24","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee M.M.R, Tajbakhsh N, Liang J, (2018) UNet++: A nested U-Net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer), pp. 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"6684_CR25","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.neucom.2020.08.086","volume":"453","author":"Z Cao","year":"2021","unstructured":"Cao Z, Yu B, Lei B, Ying H, Zhang X, Chen DZ, Wu J (2021) Cascaded SE-ResUnet for segmentation of thoracic organs at risk. Neurocomputing 453:357","journal-title":"Neurocomputing"},{"key":"6684_CR26","unstructured":"Oktay O, Schlemper J, Folgoc L.L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N.Y, Kainz B, et al. (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"6684_CR27","doi-asserted-by":"crossref","unstructured":"Fan D.P, Ji G.P, Zhou T, Chen G, Fu H, Shen J, Shao L, (2020) Pranet: Parallel reverse attention network for polyp segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer), pp. 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"6684_CR28","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","volume":"117","author":"MA Al-Antari","year":"2018","unstructured":"Al-Antari MA, Al-Masni MA, Choi MT, Han SM, Kim TS (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Medi Inform 117:44","journal-title":"Int J Medi Inform"},{"issue":"1","key":"6684_CR29","doi-asserted-by":"publisher","first-page":"20","DOI":"10.5152\/dir.2020.20205","volume":"27","author":"M Blain","year":"2021","unstructured":"Blain M, Kassin MT, Varble N, Wang X, Xu Z, Xu D, Carrafiello G, Vespro V, Stellato E, Ierardi AM et al (2021) Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images. Diagnost Intervent Radiol 27(1):20","journal-title":"Diagnost Intervent Radiol"},{"key":"6684_CR30","doi-asserted-by":"crossref","unstructured":"Moeskops P, Wolterink J.M, van der Velden B.H, Gilhuijs K.G, Leiner T, Viergever M.A, I\u0161gum I (2016) Deep learning for multi-task medical image segmentation in multiple modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer), pp. 478\u2013486","DOI":"10.1007\/978-3-319-46723-8_55"},{"key":"6684_CR31","doi-asserted-by":"crossref","unstructured":"Trullo R, Petitjean C, Ruan S, Dubray B, Nie D, Shen D, (2017) Segmentation of organs at risk in thoracic CT images using a sharpmask architecture and conditional random fields. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE), pp. 1003\u20131006","DOI":"10.1109\/ISBI.2017.7950685"},{"issue":"1","key":"6684_CR32","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, \u00c7i\u00e7ek \u00d6, Abdulkadir A, Marrakchi Y, B\u00f6hm A, Deubner J, J\u00e4ckel Z, Seiwald K et al (2019) U-Net: Deep learning for cell counting, detection, and morphometry. Nature Methods 16(1):67","journal-title":"Nature Methods"},{"issue":"2","key":"6684_CR33","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/mp.13300","volume":"46","author":"W Zhu","year":"2019","unstructured":"Zhu W, Huang Y, Zeng L, Chen X, Liu Y, Qian Z, Du N, Fan W, Xie X (2019) AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys 46(2):576","journal-title":"Med Phys"},{"key":"6684_CR34","doi-asserted-by":"publisher","first-page":"113131","DOI":"10.1016\/j.eswa.2019.113131","volume":"145","author":"L Song","year":"2020","unstructured":"Song L, Geoffrey K, Kaijian H (2020) Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation. Exp Syst Appl 145:113131","journal-title":"Exp Syst Appl"},{"key":"6684_CR35","doi-asserted-by":"crossref","unstructured":"Mehta R, Sivaswamy J, (2017) M-Net: A convolutional neural network for deep brain structure segmentation. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE), pp. 437\u2013440","DOI":"10.1109\/ISBI.2017.7950555"},{"issue":"3","key":"6684_CR36","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1134\/S1054661817030269","volume":"27","author":"A Sevastopolsky","year":"2017","unstructured":"Sevastopolsky A (2017) Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Patt Recognit Image Anal 27(3):618","journal-title":"Patt Recognit Image Anal"},{"key":"6684_CR37","doi-asserted-by":"crossref","unstructured":"Chen S, Tan X, Wang B, Hu X (2018) Reverse attention for salient object detection. Proceedings of the European Conference on Computer Vision (ECCV) pp. 234\u2013250","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"6684_CR38","unstructured":"Gao S, Cheng M.M, Zhao K, Zhang X.Y, Yang M.H, Torr P.H (2019) Res2net: A new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence"},{"issue":"4","key":"6684_CR39","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Patt Anal Mach Intell 40(4):834","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"6684_CR40","unstructured":"He K, Zhang X, Ren S, Sun J (2016) U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06684-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06684-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06684-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:15:44Z","timestamp":1744154144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06684-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":40,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["6684"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06684-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"15 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}