{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:46:47Z","timestamp":1758401207156,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972351","62111530300"],"award-info":[{"award-number":["61972351","62111530300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Public Welfare Technology Research Project of Zhejiang Province","award":["LGF19G010002","LGF20G010002"],"award-info":[{"award-number":["LGF19G010002","LGF20G010002"]}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"crossref","award":["2022C01005"],"award-info":[{"award-number":["2022C01005"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00521-022-07379-y","type":"journal-article","created":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T13:05:41Z","timestamp":1653743141000},"page":"17371-17380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Heterogeneous data fusion and loss function design for tooth point cloud segmentation"],"prefix":"10.1007","volume":"34","author":[{"given":"Dongsheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0596-1209","authenticated-orcid":false,"given":"Yan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Judith","family":"Gelernter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"7379_CR1","doi-asserted-by":"crossref","unstructured":"Berman M, Triki AR, Blaschko, MB (2018) The lov\u00e1sz-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4413\u20134421","DOI":"10.1109\/CVPR.2018.00464"},{"key":"7379_CR2","unstructured":"Caliva F, Iriondo C, Martinez AM et al. (2019) Distance map loss penalty term for semantic segmentation. In: Proceedings of Medical Imaging with Deep Learning, 2413\u20132422"},{"key":"7379_CR3","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.neucom.2020.12.067","volume":"432","author":"Y Cui","year":"2021","unstructured":"Cui Y, Liu X, Liu H et al (2021) Geometric attentional dynamic graph convolutional neural networks for point cloud analysis. Neurocomputing 432:300\u2013310","journal-title":"Neurocomputing"},{"key":"7379_CR4","doi-asserted-by":"publisher","first-page":"101949","DOI":"10.1016\/j.media.2020.101949","volume":"69","author":"Z Cui","year":"2021","unstructured":"Cui Z, Li C, Chen N et al (2021) Tsegnet: an efficient and accurate tooth segmentation network on 3d dental model. Med Image Anal 69:101949","journal-title":"Med Image Anal"},{"key":"7379_CR5","doi-asserted-by":"crossref","unstructured":"Dong X, Yang Y (2019) One-shot neural architecture search via self-evaluated template network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 3681\u20133690","DOI":"10.1109\/ICCV.2019.00378"},{"key":"7379_CR6","doi-asserted-by":"crossref","unstructured":"Elsken T, Metzen, JH, Hutter F (2019) Efficient multi-objective neural architecture search via lamarckian evolution. In: International Conference on Learning Representations, 551\u2013562","DOI":"10.1007\/978-3-030-05318-5_3"},{"issue":"55","key":"7379_CR7","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken T, Metzen JH, Hutter F et al (2019) Neural architecture search: a survey. J Mach Learn Res 20(55):1\u201321","journal-title":"J Mach Learn Res"},{"issue":"3","key":"7379_CR8","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1177\/00220345211040459","volume":"101","author":"J Hao","year":"2021","unstructured":"Hao J, Liao W, Zhang Y, Peng J, Zhao Z, Chen Z, Zhou B, Feng Y, Fang B, Liu Z et al (2021) Toward clinically applicable 3-dimensional tooth segmentation via deep learning. J Dental Res 101(3):304\u2013311","journal-title":"J Dental Res"},{"key":"7379_CR9","doi-asserted-by":"crossref","unstructured":"He J, Wang S, Li J (2020) Tooth point cloud segmentation of dental model based on region growing. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture, 489\u2013492","DOI":"10.1145\/3421766.3421802"},{"key":"7379_CR10","doi-asserted-by":"publisher","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","volume":"212","author":"X He","year":"2021","unstructured":"He X, Zhao K, Chu X (2021) Automl: a survey of the state-of-the-art. Knowledge-Based Syst 212:106622","journal-title":"Knowledge-Based Syst"},{"key":"7379_CR11","unstructured":"Kandasamy K, Neiswanger W, Schneider J et al (2018) Neural architecture search with bayesian optimisation and optimal transport. In: Advances in Neural Information Processing Systems, 1245\u20131253"},{"issue":"2","key":"7379_CR12","doi-asserted-by":"publisher","first-page":"490","DOI":"10.3390\/app10020490","volume":"10","author":"T Kim","year":"2020","unstructured":"Kim T, Cho Y, Kim D et al (2020) Tooth segmentation of 3d scan data using generative adversarial networks. Appl Sci 10(2):490","journal-title":"Appl Sci"},{"key":"7379_CR13","doi-asserted-by":"crossref","unstructured":"Li C, Yuan X, Lin C et al (2019) Am-lfs: Automl for loss function search. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 8410\u20138419","DOI":"10.1109\/ICCV.2019.00850"},{"key":"7379_CR14","unstructured":"Li H, Fu T, Dai J et al (2021) Autoloss-zero: Searching loss functions from scratch for generic tasks. arXiv preprint arXiv:2103.14026"},{"key":"7379_CR15","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.neucom.2021.01.091","volume":"437","author":"H Li","year":"2021","unstructured":"Li H, Sun Z, Wu Y et al (2021) Semi-supervised point cloud segmentation using self-training with label confidence prediction. Neurocomputing 437:227\u2013237","journal-title":"Neurocomputing"},{"key":"7379_CR16","unstructured":"Li H, Tao C, Zhu X et al (2020) Auto seg-loss: searching metric surrogates for semantic segmentation. In: International Conference on Learning Representations, 2410\u20132419"},{"key":"7379_CR17","unstructured":"Li Y, Bu R, Sun M et al (2018) Pointcnn: Convolution on x-transformed points. In: Advances in Neural Information Processing Systems, 820\u2013830"},{"issue":"7","key":"7379_CR18","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1109\/TMI.2020.2971730","volume":"39","author":"C Lian","year":"2020","unstructured":"Lian C, Wang L, Wu TH et al (2020) Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3d intraoral scanners. IEEE Trans Med Imag 39(7):2440\u20132450","journal-title":"IEEE Trans Med Imag"},{"key":"7379_CR19","doi-asserted-by":"crossref","unstructured":"Liu C, Zoph B, Neumann M et al (2018) Progressive neural architecture search. In: Proceedings of the European Conference on Computer Vision, 19\u201334","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"7379_CR20","unstructured":"Liu H, Simonyan K, Yang Y (2019) Darts: Differentiable architecture search. In: International Conference on Learning Representations, 651\u2013662"},{"key":"7379_CR21","unstructured":"Liu P, Zhang G, Wang B et al (2021) Loss function discovery for object detection via convergence-simulation driven search. In: International Conference on Learning Representations, 731\u2013732"},{"issue":"7","key":"7379_CR22","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/cgf.14143","volume":"39","author":"Q Ma","year":"2020","unstructured":"Ma Q, Wei G, Zhou Y et al (2020) Srf-net: Spatial relationship feature network for tooth point cloud classification. Computer Graphics Forum 39(7):267\u2013277","journal-title":"Computer Graphics Forum"},{"key":"7379_CR23","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision, 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"7379_CR24","unstructured":"Paszke A, Gross S, Massa F et al (2019) Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, 8026\u20138037"},{"key":"7379_CR25","doi-asserted-by":"crossref","unstructured":"Qin X, Zhang Z, Huang C et al (2019) Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7479\u20137489","DOI":"10.1109\/CVPR.2019.00766"},{"key":"7379_CR26","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y et al (2019) Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, 4780\u20134789","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"7379_CR27","unstructured":"Real E, Moore S, Selle A et al (2017) Large-scale evolution of image classifiers. In: International Conference on Machine Learning, 2902\u20132911"},{"key":"7379_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O, Fischer, P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7379_CR29","doi-asserted-by":"crossref","unstructured":"Sun D, Pei Y, Li P et al (2020) Automatic tooth segmentation and dense correspondence of 3d dental model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 703\u2013712","DOI":"10.1007\/978-3-030-59719-1_68"},{"key":"7379_CR30","doi-asserted-by":"crossref","unstructured":"Sun D, Pei Y, Song G et al (2020) Tooth segmentation and labeling from digital dental casts. In: IEEE International Symposium on Biomedical Imaging, 669\u2013673","DOI":"10.1109\/ISBI45749.2020.9098397"},{"key":"7379_CR31","doi-asserted-by":"publisher","first-page":"84817","DOI":"10.1109\/ACCESS.2019.2924262","volume":"7","author":"S Tian","year":"2019","unstructured":"Tian S, Dai N, Zhang B et al (2019) Automatic classification and segmentation of teeth on 3d dental model using hierarchical deep learning networks. IEEE Access 7:84817\u201384828","journal-title":"IEEE Access"},{"issue":"10","key":"7379_CR32","first-page":"1","volume":"21","author":"Y Tian","year":"2021","unstructured":"Tian Y, Chen T, Cheng G et al (2021) Global context assisted structure-aware vehicle retrieval. IEEE Trans Intell Trans Syst 21(10):1\u201310","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"7379_CR33","doi-asserted-by":"publisher","first-page":"107158","DOI":"10.1016\/j.patcog.2019.107158","volume":"100","author":"Y Tian","year":"2020","unstructured":"Tian Y, Cheng G, Gelernter J et al (2020) Joint temporal context exploitation and active learning for video segmentation. Pattern Recogn 100:107158","journal-title":"Pattern Recogn"},{"issue":"12","key":"7379_CR34","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.1109\/TITS.2018.2886283","volume":"20","author":"Y Tian","year":"2019","unstructured":"Tian Y, Gelernter J, Wang X et al (2019) Traffic sign detection using a multi-scale recurrent attention network. IEEE Trans Intell Trans Syst 20(12):4466\u20134475","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"7379_CR35","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1613\/jair.1.11338","volume":"64","author":"Y Tian","year":"2019","unstructured":"Tian Y, Wang X, Wu J et al (2019) Multi-scale hierarchical residual network for dense captioning. J Artif Intell Res 64:181\u2013196","journal-title":"J Artif Intell Res"},{"key":"7379_CR36","first-page":"4780","volume":"33","author":"Y Tian","year":"2021","unstructured":"Tian Y, Zhang Y, We-Gang C et al (2021) 3d tooth instance segmentation learning objectness and affinity in point cloud. ACM Trans Multimedia Comput Commun Appl 33:4780\u20134789","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"7379_CR37","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2020.07.078","volume":"417","author":"Y Tian","year":"2020","unstructured":"Tian Y, Zhang Y, Zhou D et al (2020) Triple attention network for video segmentation. Neurocomputing 417:202\u2013211","journal-title":"Neurocomputing"},{"key":"7379_CR38","unstructured":"Veli\u010dkovi\u0107 P, Cucurull, G, Casanova A et al (2018) Graph attention networks. In: The International Conference on Learning Representations, 1780\u20131789"},{"key":"7379_CR39","doi-asserted-by":"crossref","unstructured":"Verma N, Boyer E, Verbeek J (2018) Feastnet: Feature-steered graph convolutions for 3d shape analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2598\u20132606","DOI":"10.1109\/CVPR.2018.00275"},{"key":"7379_CR40","unstructured":"Wang X, Wang S, Chi C et al (2020) Loss function search for face recognition. In: International Conference on Machine Learning, 10029\u201310038"},{"key":"7379_CR41","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2020.03.086","volume":"402","author":"Z Xie","year":"2020","unstructured":"Xie Z, Chen J, Peng B (2020) Point clouds learning with attention-based graph convolution networks. Neurocomputing 402:245\u2013255","journal-title":"Neurocomputing"},{"issue":"7","key":"7379_CR42","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1109\/TVCG.2018.2839685","volume":"25","author":"X Xu","year":"2018","unstructured":"Xu X, Liu C, Zheng Y (2018) 3d tooth segmentation and labeling using deep convolutional neural networks. IEEE Trans Vis Computer Graph 25(7):2336\u20132348","journal-title":"IEEE Trans Vis Computer Graph"},{"key":"7379_CR43","unstructured":"Zanjani FG, Moin DA, Verheij B et al. (2019) Deep learning approach to semantic segmentation in 3d point cloud intra-oral scans of teeth. In: International Conference on Medical Imaging with Deep Learning, 557\u2013571"},{"key":"7379_CR44","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C et al (2019) Heterogeneous graph neural network. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"7379_CR45","doi-asserted-by":"publisher","first-page":"101071","DOI":"10.1016\/j.gmod.2020.101071","volume":"109","author":"J Zhang","year":"2020","unstructured":"Zhang J, Li C, Song Q et al (2020) Automatic 3d tooth segmentation using convolutional neural networks in harmonic parameter space. Graphical Models 109:101071","journal-title":"Graphical Models"},{"key":"7379_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, L, Zhao Y, Meng D et al (2021) Tsgcnet: Discriminative geometric feature learning with two-stream graph convolutional network for 3d dental model segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 6699\u20136708","DOI":"10.1109\/CVPR46437.2021.00663"},{"key":"7379_CR47","unstructured":"Zoph B, Le QV (2017) Neural architecture search with reinforcement learning. In: International Conference on Learning Representations, 751\u2013762"},{"key":"7379_CR48","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J et al (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogn, 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07379-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07379-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07379-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T15:13:43Z","timestamp":1663946023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07379-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,28]]},"references-count":48,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["7379"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07379-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,5,28]]},"assertion":[{"value":"21 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}