{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:50:59Z","timestamp":1779270659474,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872143"],"award-info":[{"award-number":["61872143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Accurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.<\/jats:p>","DOI":"10.1186\/s13640-021-00574-2","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T11:02:37Z","timestamp":1636801357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification"],"prefix":"10.1186","volume":"2021","author":[{"given":"Ling","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-7066","authenticated-orcid":false,"given":"Hongqing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"issue":"9","key":"574_CR1","doi-asserted-by":"publisher","first-page":"170070","DOI":"10.1098\/rsob.170070","volume":"7","author":"S Blandin Knight","year":"2017","unstructured":"S. Blandin Knight, P.A. Crosbie, H. Balata, J. Chudziak, T. Hussell, C. Dive, Progress and prospects of early detection in lung cancer. Open Biol. 7(9), 170070 (2017)","journal-title":"Open Biol."},{"issue":"8","key":"574_CR2","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1049\/iet-ipr.2019.0248","volume":"14","author":"J Zheng","year":"2020","unstructured":"J. Zheng, D. Yang, Y. Zhu, W. Gu, B. Zheng, C. Bai, L. Zhao, H. Shi, J. Hu, S. Lu et al., Pulmonary nodule risk classification in adenocarcinoma from CT images using deep CNN with scale transfer module. IET Image Process. 14(8), 1481\u20131489 (2020)","journal-title":"IET Image Process."},{"key":"574_CR3","unstructured":"K. Sun, Y. Zhao, B. Jiang, T. Cheng, B. Xiao, D. Liu, Y. Mu, X. Wang, W. Liu, J. Wang, High-resolution representations for labeling pixels and regions. arXiv preprint arXiv:1904.04514 (2019)"},{"issue":"1","key":"574_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-019-0467-y","volume":"2019","author":"S Baghersalimi","year":"2019","unstructured":"S. Baghersalimi, B. Bozorgtabar, P. Schmid-Saugeon, H.K. Ekenel, J.-P. Thiran, DermoNet: Densely linked convolutional neural network for efficient skin lesion segmentation. EURASIP J. Image Video Process. 2019(1), 1\u201310 (2019)","journal-title":"EURASIP J. Image Video Process."},{"key":"574_CR5","doi-asserted-by":"publisher","first-page":"103115","DOI":"10.1016\/j.compind.2019.07.005","volume":"112","author":"R Miao","year":"2019","unstructured":"R. Miao, Y. Gao, L. Ge, Z. Jiang, J. Zhang, Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network. Comput. Ind. 112, 103115 (2019)","journal-title":"Comput. Ind."},{"key":"574_CR6","doi-asserted-by":"crossref","unstructured":"S. Chen, Y. Wang, Pulmonary nodule segmentation in computed tomography with an encoder-decoder architecture. In: International Conference on Information Technology in Medicine and Education (ITME), pp. 157\u2013162 (2019)","DOI":"10.1109\/ITME.2019.00045"},{"key":"574_CR7","unstructured":"N.V. Keetha, C.S.R. Annavarapu, et al., U-Det: A modified U-Net architecture with bidirectional feature network for lung nodule segmentation. arXiv preprint arXiv:2003.09293 (2020)"},{"key":"574_CR8","doi-asserted-by":"publisher","first-page":"105792","DOI":"10.1016\/j.cmpb.2020.105792","volume":"198","author":"G Pezzano","year":"2021","unstructured":"G. Pezzano, V.R. Ripoll, P. Radeva, CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Comput. Meth. Prog. Bio. 198, 105792 (2021)","journal-title":"Comput. Meth. Prog. Bio."},{"key":"574_CR9","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ejmp.2019.06.003","volume":"63","author":"H Liu","year":"2019","unstructured":"H. Liu, H. Cao, E. Song, G. Ma, X. Xu, R. Jin, Y. Jin, C.-C. Hung, A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Phys. Medica 63, 112\u2013121 (2019)","journal-title":"Phys. Medica"},{"key":"574_CR10","doi-asserted-by":"crossref","unstructured":"H. Yan, H. Lu, M. Ye, K. Yan, Y. Xu, Q. Jin, Improved Mask R-CNN for lung nodule segmentation. In: International Conference on Information Technology in Medicine and Education (ITME), pp. 137\u2013141 (2019)","DOI":"10.1109\/ITME.2019.00041"},{"key":"574_CR11","doi-asserted-by":"publisher","first-page":"128796","DOI":"10.1109\/ACCESS.2019.2939850","volume":"7","author":"W Wang","year":"2019","unstructured":"W. Wang, R. Feng, J. Chen, Y. Lu, T. Chen, H. Yu, D.Z. Chen, J. Wu, Nodule-plus R-CNN and deep self-paced active learning for 3D instance segmentation of pulmonary nodules. IEEE Access 7, 128796\u2013128805 (2019)","journal-title":"IEEE Access"},{"key":"574_CR12","doi-asserted-by":"publisher","first-page":"26457","DOI":"10.1109\/ACCESS.2020.2971542","volume":"8","author":"Y Sun","year":"2020","unstructured":"Y. Sun, J. Tang, W. Lei, D. He, 3D segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access 8, 26457\u201326467 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"574_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40644-020-00331-0","volume":"20","author":"X Dong","year":"2020","unstructured":"X. Dong, S. Xu, Y. Liu, A. Wang, M.I. Saripan, L. Li, X. Zhang, L. Lu, Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation. Cancer Imaging 20(1), 1\u201313 (2020)","journal-title":"Cancer Imaging"},{"key":"574_CR14","unstructured":"I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"issue":"3","key":"574_CR15","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1002\/mp.13349","volume":"46","author":"Y Qin","year":"2019","unstructured":"Y. Qin, H. Zheng, X. Huang, J. Yang, Y.-M. Zhu, Pulmonary nodule segmentation with CT sample synthesis using adversarial networks. Med. Phys. 46(3), 1218\u20131229 (2019)","journal-title":"Med. Phys."},{"issue":"2","key":"574_CR16","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s12194-020-00564-5","volume":"13","author":"Y Onishi","year":"2020","unstructured":"Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, H. Toyama, K. Imaizumi, H. Fujita, Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes. Radiol. Phys. Technol. 13(2), 160\u2013169 (2020)","journal-title":"Radiol. Phys. Technol."},{"key":"574_CR17","doi-asserted-by":"crossref","unstructured":"H. Shi, J. Lu, Q. Zhou, A novel data augmentation method using style-based GAN for robust pulmonary nodule segmentation. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 2486\u20132491 (2020)","DOI":"10.1109\/CCDC49329.2020.9164303"},{"key":"574_CR18","doi-asserted-by":"crossref","unstructured":"D. Nie, Y. Gao, L. Wang, D. Shen, ASDNet: Attention based semi-supervised deep networks for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 370\u2013378 (2018)","DOI":"10.1007\/978-3-030-00937-3_43"},{"key":"574_CR19","doi-asserted-by":"publisher","first-page":"103884","DOI":"10.1016\/j.compbiomed.2020.103884","volume":"123","author":"C Decourt","year":"2020","unstructured":"C. Decourt, L. Duong, Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI. Comput. Biol. Med. 123, 103884 (2020)","journal-title":"Comput. Biol. Med."},{"key":"574_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.media.2018.08.005","volume":"50","author":"Z Han","year":"2018","unstructured":"Z. Han, B. Wei, A. Mercado, S. Leung, S. Li, Spine-GAN: Semantic segmentation of multiple spinal structures. Med. Image Anal. 50, 23\u201335 (2018)","journal-title":"Med. Image Anal."},{"key":"574_CR21","doi-asserted-by":"crossref","unstructured":"Y. Sun, C. Zhou, Y. Fu, X. Xue, Parasitic GAN for semi-supervised brain tumor segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1535\u20131539 (2019)","DOI":"10.1109\/ICIP.2019.8803073"},{"issue":"3","key":"574_CR22","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1007\/s11045-020-00703-6","volume":"31","author":"Y Wang","year":"2020","unstructured":"Y. Wang, H. Zhang, K.J. Chae, Y. Choi, G.Y. Jin, S.-B. Ko, Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography. Multidimens. Syst. Signal Process. 31(3), 1163\u20131183 (2020)","journal-title":"Multidimens. Syst. Signal Process."},{"key":"574_CR23","doi-asserted-by":"crossref","unstructured":"Y. Zhang, J. Zhang, L. Zhao, X. Wei, Q. Zhang, Classification of benign and malignant pulmonary nodules based on deep learning. In: International Conference on Information Science and Control Engineering (ICISCE), pp. 156\u2013160 (2018)","DOI":"10.1109\/ICISCE.2018.00042"},{"issue":"1","key":"574_CR24","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s11548-019-02092-z","volume":"15","author":"Y Onishi","year":"2020","unstructured":"Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, H. Toyama, K. Imaizumi, H. Fujita, Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int. J. Comput. Assist. Radiol. Surg. 15(1), 173\u2013178 (2020)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"2","key":"574_CR25","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/JSAC.2020.3020657","volume":"39","author":"C Tong","year":"2020","unstructured":"C. Tong, B. Liang, Q. Su, M. Yu, J. Hu, A.K. Bashir, Z. Zheng, Pulmonary nodule classification based on heterogeneous features learning. IEEE J. Sel. Areas Commun. 39(2), 574\u2013581 (2020)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"574_CR26","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Chen, C., Huang, B., Luo, Y., Ma, J., Jiang, J.: Multi-scale progressive fusion network for single image deraining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8346\u20138355 (2020)","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"574_CR27","doi-asserted-by":"crossref","unstructured":"H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, M.-H. Yang, Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2157\u20132167 (2020)","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"574_CR28","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 630\u2013645 (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"574_CR29","doi-asserted-by":"crossref","unstructured":"K. Sun, B. Xiao, D. Liu, J. Wang, Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5693\u20135703 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"issue":"12","key":"574_CR30","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"574_CR31","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"574_CR32","doi-asserted-by":"crossref","unstructured":"Z. Tian, T. He, C. Shen, Y. Yan, Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3126\u20133135 (2019)","DOI":"10.1109\/CVPR.2019.00324"},{"issue":"1","key":"574_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-019-0465-0","volume":"2019","author":"Z Zhao","year":"2019","unstructured":"Z. Zhao, Q. Sun, H. Yang, H. Qiao, Z. Wang, D.O. Wu, Compression artifacts reduction by improved generative adversarial networks. EURASIP J. Image Video Process. 2019(1), 1\u20137 (2019)","journal-title":"EURASIP J. Image Video Process."},{"key":"574_CR34","doi-asserted-by":"crossref","unstructured":"Y. Yuan, J. Xie, X. Chen, J. Wang, Segfix: Model-agnostic boundary refinement for segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 489\u2013506 (2020)","DOI":"10.1007\/978-3-030-58610-2_29"},{"key":"574_CR35","unstructured":"M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial networks. In: International Conference on Machine Learning (ICML), pp. 214\u2013223 (2017)"},{"issue":"2","key":"574_CR36","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"S.G. Armato III., G. McLennan, L. Bidaut, M.F. McNitt-Gray, C.R. Meyer, A.P. Reeves, B. Zhao, D.R. Aberle, C.I. Henschke, E.A. Hoffman et al., The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"574_CR37","unstructured":"J. Zhuang, T. Tang, S. Tatikonda, N. Dvornek, Y. Ding, X. Papademetris, J.S. Duncan, Adabelief optimizer: Adapting stepsizes by the belief in observed gradients. arXiv preprint arXiv:2010.07468 (2020)"},{"key":"574_CR38","unstructured":"A. Paszke, A. Chaurasia, S. Kim, E. Culurciello, ENet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)"},{"key":"574_CR39","doi-asserted-by":"crossref","unstructured":"H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"574_CR40","doi-asserted-by":"crossref","unstructured":"Z. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, J. Liang, Unet++: A nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"574_CR41","doi-asserted-by":"crossref","unstructured":"H. Li, P. Xiong, H. Fan, J. Sun, DFANet: Deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9522\u20139531 (2019)","DOI":"10.1109\/CVPR.2019.00975"},{"key":"574_CR42","unstructured":"R.P. Poudel, S. Liwicki, R. Cipolla, Fast-SCNN: Fast semantic segmentation network. arXiv preprint arXiv:1902.04502 (2019)"},{"issue":"1","key":"574_CR43","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/LRA.2020.3039744","volume":"6","author":"P Hu","year":"2020","unstructured":"P. Hu, F. Perazzi, F.C. Heilbron, O. Wang, Z. Lin, K. Saenko, S. Sclaroff, Real-time semantic segmentation with fast attention. IEEE Robot. Autom. Lett. 6(1), 263\u2013270 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"574_CR44","doi-asserted-by":"crossref","unstructured":"Q. Hou, L. Zhang, M.-M. Cheng, J. Feng, Strip pooling: Rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4003\u20134012 (2020)","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"574_CR45","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"574_CR46","doi-asserted-by":"publisher","first-page":"32510","DOI":"10.1109\/ACCESS.2019.2903587","volume":"7","author":"W Zuo","year":"2019","unstructured":"W. Zuo, F. Zhou, Z. Li, L. Wang, Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access 7, 32510\u201332521 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"574_CR47","doi-asserted-by":"publisher","first-page":"368","DOI":"10.26599\/TST.2019.9010010","volume":"25","author":"G Zheng","year":"2019","unstructured":"G. Zheng, G. Han, N.Q. Soomro, An inception module CNN classifiers fusion method on pulmonary nodule diagnosis by signs. Tsinghua Sci. Technol. 25(3), 368\u2013383 (2019)","journal-title":"Tsinghua Sci. Technol."},{"key":"574_CR48","doi-asserted-by":"crossref","unstructured":"S. Akila Agnes, J. Anitha, Automatic 2D lung nodule patch classification using deep neural networks. In: International Conference on Inventive Systems and Control (ICISC), pp. 500\u2013504 (2020)","DOI":"10.1109\/ICISC47916.2020.9171183"}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-021-00574-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13640-021-00574-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-021-00574-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T11:07:33Z","timestamp":1636801653000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-021-00574-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,13]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["574"],"URL":"https:\/\/doi.org\/10.1186\/s13640-021-00574-2","relation":{},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,13]]},"assertion":[{"value":"3 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 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":"Approved.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"34"}}