{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T19:16:22Z","timestamp":1784920582816,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001380"],"award-info":[{"award-number":["62001380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00521-021-06546-x","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T14:08:06Z","timestamp":1632924486000},"page":"11583-11597","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Rib segmentation algorithm for X-ray image based on unpaired sample augmentation and multi-scale network"],"prefix":"10.1007","volume":"35","author":[{"given":"Hongyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dandan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songtao","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"issue":"12","key":"6546_CR1","doi-asserted-by":"publisher","first-page":"3595","DOI":"10.1109\/JBHI.2020.3037127","volume":"24","author":"S Tabik","year":"2020","unstructured":"Tabik S, G\u00f3mez-R\u00edos A, Mart\u00edn-Rodr\u00edguez JL, Sevillano-Garc\u00eda I, Rey-Area M, Charte D, Guirado E, Su\u00e1rez JL, Luengo J, Valero-Gonz\u00e1lez MA, Garc\u00eda-Villanova P, Olmedo-S\u00e1nchez E, Herrera F (2020) Covidgr dataset and covid-sdnet methodology for predicting covid-19 based on chest x-ray images. IEEE J Biomed Health Inform 24(12):3595\u20133605","journal-title":"IEEE J Biomed Health Inform"},{"key":"6546_CR2","doi-asserted-by":"publisher","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","volume":"140","author":"S Hassantabar","year":"2020","unstructured":"Hassantabar S, Ahmadi M, Sharifi A (2020) Diagnosis and detection of infected tissue of covid-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos, Solitons and Fractals 140:110170","journal-title":"Chaos, Solitons and Fractals"},{"key":"6546_CR3","unstructured":"Wessel J, Heinrich M.P, von Berg J, Franz A, Saalbach A (2019) Sequential rib labeling and segmentation in chest x-ray using mask r-cnn, arXiv preprint arXiv:1908.08329,"},{"key":"6546_CR4","unstructured":"Li H, Han H, Li Z, Wang L, Wu Z, Lu J, Zhou S.K (2020) High-Resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors,  IEEE Transactions on Medical Imaging, pp. 1\u20131, . [Online]. Available: https:\/\/ieeexplore.ieee.org\/document\/9058664\/"},{"key":"6546_CR5","doi-asserted-by":"crossref","unstructured":"Oliveira H,Mota V, Machado A.M.C,Santos J.A.d, (2020)From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays, Pattern Recognition Letters, vol. 140, pp. 10\u201317,","DOI":"10.1016\/j.patrec.2020.09.021"},{"key":"6546_CR6","doi-asserted-by":"crossref","unstructured":"Juh\u00e1sz S, Horv\u00e1th A, Nikh\u00e1zy L, Horv\u00e1th G,Horv\u00e1th A (2010)Segmentation of Anatomical Structures on Chest Radiographs, in XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010.Berlin, Heidelberg: Springer Berlin Heidelberg, , vol.29, pp. 359\u2013362","DOI":"10.1007\/978-3-642-13039-7_90"},{"key":"6546_CR7","doi-asserted-by":"publisher","first-page":"137794","DOI":"10.1109\/ACCESS.2019.2941511","volume":"7","author":"T Peng","year":"2019","unstructured":"Peng T, Wang Y, Xu TC, Chen X (2019) Segmentation of lung in chest radiographs using hull and closed polygonal line method. IEEE Access 7:137794\u2013137810","journal-title":"IEEE Access"},{"key":"6546_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Y,Miao S, Mansi T, Liao R (2018) Task driven generative modeling for unsupervised domain adaptation: Application to x-ray image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 599\u2013607","DOI":"10.1007\/978-3-030-00934-2_67"},{"key":"6546_CR9","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1117\/12.387746","volume":"3979","author":"B van Ginneken","year":"2000","unstructured":"van Ginneken B, ter HaarRomeny BM (2000) Automatic delineation of ribs in frontal chest radiographs. Med Imag 2000 Image Process SPIE 3979:825\u2013836","journal-title":"Med Imag 2000 Image Process SPIE"},{"key":"6546_CR10","doi-asserted-by":"crossref","unstructured":"Lee J, Reeves A.P.(2010) Segmentation of individual ribs from low-dose chest CT, in Medical Imaging : Computer-Aided Diagnosis, vol. 7624, International Society for Optics and Photonics. SPIE, 2010, pp. 1001\u20131008","DOI":"10.1117\/12.844565"},{"key":"6546_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.compmedimag.2016.04.002","volume":"51","author":"S Candemir","year":"2016","unstructured":"Candemir S, Jaeger S, Antani S, Bagci U, Folio LR, Xu Z, Thoma G (2016) Atlas-based rib-bone detection in chest x-rays. Comput Med Imaging Graph 51:32\u20139","journal-title":"Comput Med Imaging Graph"},{"key":"6546_CR12","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1016\/j.ins.2021.08.042","volume":"577","author":"A Ali","year":"2021","unstructured":"Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852\u2013870","journal-title":"Inf Sci"},{"key":"6546_CR13","doi-asserted-by":"crossref","unstructured":"Ali A, Zhu Y, Zakarya M (2021)A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multim Tools Appl. No 2","DOI":"10.1007\/s11042-020-10486-4"},{"key":"6546_CR14","doi-asserted-by":"crossref","unstructured":"Ali A, Zhu Y, Chen Q, Yu J, Cai H(2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp. 125\u2013132","DOI":"10.1109\/ICPADS47876.2019.00025"},{"key":"6546_CR15","doi-asserted-by":"publisher","first-page":"5166","DOI":"10.1109\/TSP.2020.3021232","volume":"68","author":"D Liu","year":"2020","unstructured":"Liu D, Chen B, Chin T-J, Rutten MG (2020) Topological sweep for multi-target detection of geostationary space objects. IEEE Trans Signal Process 68:5166\u20135177","journal-title":"IEEE Trans Signal Process"},{"key":"6546_CR16","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.neucom.2019.04.095","volume":"398","author":"S Ding","year":"2020","unstructured":"Ding S, Qu S, Xi Y, Wan S (2020) Stimulus-driven and concept-driven analysis for image caption generation. Neurocomputing 398:520\u2013530","journal-title":"Neurocomputing"},{"issue":"4","key":"6546_CR17","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32(4):582\u2013596","journal-title":"J Digit Imaging"},{"issue":"4","key":"6546_CR18","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1109\/JBHI.2019.2891526","volume":"23","author":"Y Zhao","year":"2019","unstructured":"Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, Piraud M, Menze B (2019) Knowledge-aided convolutional neural network for small organ segmentation. IEEE J Biomed Health Inform 23(4):1363\u20131373","journal-title":"IEEE J Biomed Health Inform"},{"key":"6546_CR19","first-page":"1","volume":"07","author":"W Wang","year":"2020","unstructured":"Wang W, Feng H, Bu Q, Cui L, Xie Y, Zhang A, Feng J, Zhu Z, Chen Z (2020) Mdu-net: a convolutional network for clavicle and rib segmentation from a chest radiograph. J Healthc Eng 07:1\u20139","journal-title":"J Healthc Eng"},{"key":"6546_CR20","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/ACCESS.2019.2960775","volume":"8","author":"L Huang","year":"2019","unstructured":"Huang L, Pan W, Zhang Y, Qian L, Gao N, Wu Y (2019) Data augmentation for deep learning-based radio modulation classification. IEEE Access 8:1498\u20131506","journal-title":"IEEE Access"},{"key":"6546_CR21","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.compmedimag.2016.04.002","volume":"51","author":"Xu Jaeger Stefan","year":"2016","unstructured":"Jaeger Stefan Xu, Ziyue Thoma George, Sema Candemir, Les Folio (2016) Atlas-based rib-bone detection in chest x-rays. Comput Med Imaging Graph 51:32\u201339","journal-title":"Comput Med Imaging Graph"},{"key":"6546_CR22","doi-asserted-by":"crossref","unstructured":"Zhang G, Wu H, Guo W (2016)Rib segmentation in chest radiographs by support vector machine. In: Proceedings of the 2016 International Conference on Education, Management, Computer and Society. Atlantis Press, pp. 1564\u20131567","DOI":"10.2991\/emcs-16.2016.391"},{"key":"6546_CR23","doi-asserted-by":"publisher","first-page":"105014","DOI":"10.1016\/j.cmpb.2019.105014","volume":"180","author":"Y Liu","year":"2019","unstructured":"Liu Y, Zhang X, Cai G, Chen Y, Yun Z, Feng Q, Yang W (2019) Automatic delineation of ribs and clavicles in chest radiographs using fully convolutional densenets. Comput Methods Programs Biomed 180:105014","journal-title":"Comput Methods Programs Biomed"},{"key":"6546_CR24","doi-asserted-by":"crossref","unstructured":"Zhao A, Balakrishnan G, Durand F, Guttag JV, Dalca AV (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June","DOI":"10.1109\/CVPR.2019.00874"},{"key":"6546_CR25","doi-asserted-by":"crossref","unstructured":"Ogul BB, S\u00fcmer E, Ogul H (2015) Unsupervised rib delineation in chest radiographs by an integrative approach. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, Berlin, Germany, pp 260\u2013265","DOI":"10.5220\/0005361602600265"},{"issue":"5","key":"6546_CR26","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/TMI.2006.872747","volume":"25","author":"M Loog","year":"2006","unstructured":"Loog M, Ginneken B (2006) Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans Med Imaging 25(5):602\u2013611","journal-title":"IEEE Trans Med Imaging"},{"key":"6546_CR27","doi-asserted-by":"crossref","unstructured":"Li X, Luo S, Hu Q (2015) An automatic rib segmentation method on X-ray Radiographs, in multimedia modeling. Cham: Springer International Publishing, , pp. 128\u2013139","DOI":"10.1007\/978-3-319-14445-0_12"},{"key":"6546_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu M, Xi Y, Qin G, Shen D, Yang W(2020) Xray-Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020. Cham: Springer International Publishing, , vol. 12262, pp. 678\u2013687, series Title: Lecture Notes in Computer Science","DOI":"10.1007\/978-3-030-59713-9_65"},{"key":"6546_CR29","doi-asserted-by":"publisher","first-page":"102607","DOI":"10.1016\/j.bspc.2021.102607","volume":"68","author":"H Wang","year":"2021","unstructured":"Wang H, Cao J, Feng J, Xie Y, Yang D, Chen B (2021) Mixed 2d and 3d convolutional network with multi-scale context for lesion segmentation in breast dce-mri. Biomed Signal Process Control 68:102607","journal-title":"Biomed Signal Process Control"},{"key":"6546_CR30","unstructured":"Lei Y, Fu Y, Wang T, Qiu R.L.J, Curran W.J, Liu T, Yang X (2020)Deep Learning in Multi-organ Segmentation, arXiv:2001.10619 [physics], Jan. , arXiv: 2001.10619"},{"key":"6546_CR31","doi-asserted-by":"crossref","unstructured":"Xu X, Lian C, Wang S, Wang A, Royce T, Chen R, Lian J, Shen D(2020) Asymmetrical multi-task attention U-net for the segmentation of prostate bed in CT image. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020. Cham: Springer International Publishing, , vol. 12264, pp. 470\u2013479, series Title: Lecture Notes in Computer Science","DOI":"10.1007\/978-3-030-59719-1_46"},{"key":"6546_CR32","doi-asserted-by":"crossref","unstructured":"Zhang J, Xie Y, Xia Y, Shen C (2021)Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR46437.2021.00125"},{"key":"6546_CR33","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.procs.2019.09.207","volume":"159","author":"HM Saleh","year":"2019","unstructured":"Saleh HM, Saad NH, Isa NAM (2019) Unet-overlapping chromosome segmentation using U-net: convolutional networks with test time augmentation. Proc Comput Sci 159:524\u2013533","journal-title":"Proc Comput Sci"},{"key":"6546_CR34","unstructured":"Hauberg S, Freifeld O, Larsen A.B.L, Fisher J, Hansen L (2016)Dreaming more data$$:$$ class-dependent distributions over diffeomorphisms for learned data augmentation. Artif Intell Stat, pp. 342\u2013350"},{"key":"6546_CR35","doi-asserted-by":"crossref","unstructured":"Saini M, Susan S (2019) Data augmentation of minority class with transfer learning for classification of imbalanced breast cancer dataset using inception-v3. In: Iberian Conference on Pattern Recognition and Image Analysis. Springer, , pp. 409\u2013420","DOI":"10.1007\/978-3-030-31332-6_36"},{"key":"6546_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett 125:1\u20136","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"6546_CR37","doi-asserted-by":"publisher","first-page":"031411","DOI":"10.1117\/1.JMI.6.3.031411","volume":"6","author":"S Guan","year":"2019","unstructured":"Guan S, Loew M (2019) Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J Med Imag 6(3):031411","journal-title":"J Med Imag"},{"key":"6546_CR38","first-page":"1131420","volume":"11314","author":"B Alyafi","year":"2020","unstructured":"Alyafi B, Diaz O, Mart\u00ed R (2020) Dcgans for realistic breast mass augmentation in x-ray mammography, in Medical Imaging 2020: Computer-Aided Diagnosis. Int Soc Op Photonics 11314:1131420","journal-title":"Int Soc Op Photonics"},{"key":"6546_CR39","doi-asserted-by":"crossref","unstructured":"Zhu J.Y, Park T, Isola P, Efros A.A (2017)Unpaired image-to-image translation using cycle-consistent adversarial networks. In:Proceedings of the IEEE international conference on computer vision, pp. 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"6546_CR40","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, CoRR, vol. abs\/1807.10165, . [Online]. Available: http:\/\/arxiv.org\/abs\/1807.10165","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"6546_CR41","doi-asserted-by":"crossref","unstructured":"Ma J, Chen J, Ng M, Huang R, Li Y, Li C, Yang X, Martel A.L (2021)Loss odyssey in medical image segmentation, Medical Image Analysis, vol.71, p. 102035, . [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841521000815","DOI":"10.1016\/j.media.2021.102035"},{"key":"6546_CR42","doi-asserted-by":"crossref","unstructured":"Berman M, Triki A.R, Blaschko M.B (2018)The Lov\u2019asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks, arXiv:1705.08790 [cs], Apr. , arXiv: 1705.08790. [Online]. Available: http:\/\/arxiv.org\/abs\/1705.08790","DOI":"10.1109\/CVPR.2018.00464"},{"key":"6546_CR43","unstructured":"Ronneberger O, Fischer P, Brox T(2015) U-net: Convolutional networks for biomedical image segmentation, CoRR, vol. abs\/1505.04597, . [Online]. Available: http:\/\/arxiv.org\/abs\/1505.04597"},{"key":"6546_CR44","unstructured":"Shelhamer E, Long J, Darrell T (2016)Fully convolutional networks for semantic segmentation, CoRR, vol. abs\/1605.06211, . [Online]. Available: http:\/\/arxiv.org\/abs\/1605.06211"},{"key":"6546_CR45","doi-asserted-by":"crossref","unstructured":"Chen L, Zhu Y, Papandreou G, Schroff F, Adam H(2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Vol. abs\/1802.02611. [Online]. Available: http:\/\/arxiv.org\/abs\/1802.02611","DOI":"10.1007\/978-3-030-01234-2_49"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06546-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06546-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06546-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:09:33Z","timestamp":1683824973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06546-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,29]]},"references-count":45,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["6546"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06546-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,29]]},"assertion":[{"value":"15 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 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":"The authors declare that there is no conflict of interests regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}