{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:50:19Z","timestamp":1771955419968,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031720857","type":"print"},{"value":"9783031720864","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72086-4_15","type":"book-chapter","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:34:45Z","timestamp":1727987685000},"page":"155-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Cephalometric Landmark Detection Across Ages with\u00a0Prototypical Network"],"prefix":"10.1007","author":[{"given":"Han","family":"Wu","sequence":"first","affiliation":[]},{"given":"Chong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lanzhuju","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neucom.2021.10.109","volume":"471","author":"R Chen","year":"2022","unstructured":"Chen, R., Ma, Y., Liu, L., Chen, N., Cui, Z., Wei, G., Wang, W.: Semi-supervised anatomical landmark detection via shape-regulated self-training. Neurocomputing 471, 335\u2013345 (2022)","journal-title":"Neurocomputing"},{"key":"15_CR3","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2021.08.042","volume":"464","author":"T He","year":"2021","unstructured":"He, T., Yao, J., Tian, W., Yi, Z., Tang, W., Guo, J.: Cephalometric landmark detection by considering translational invariance in the two-stage framework. Neurocomputing 464, 15\u201326 (2021)","journal-title":"Neurocomputing"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, Y., Wang, X., Tao, Y., Lin, J., Lin, H.: Cephalformer: Incorporating global structure constraint into visual features for general cephalometric landmark detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 227\u2013237. Springer (2022)","DOI":"10.1007\/978-3-031-16437-8_22"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Lee, H., Park, M., Kim, J.: Cephalometric landmark detection in dental x-ray images using convolutional neural networks. In: Medical imaging 2017: Computer-aided diagnosis. vol. 10134, pp. 494\u2013499. SPIE (2017)","DOI":"10.1117\/12.2255870"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Yu, H.J., Kim, M.j., Kim, J.W., Choi, J.: Automated cephalometric landmark detection with confidence regions using bayesian convolutional neural networks. BMC Oral Health 20, 1\u201310 (2020)","DOI":"10.1186\/s12903-020-01256-7"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Li, W., Lu, Y., Zheng, K., Liao, H., Lin, C., Luo, J., Cheng, C.T., Xiao, J., Lu, L., Kuo, C.F., et\u00a0al.: Structured landmark detection via topology-adapting deep graph learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IX 16. pp. 266\u2013283. Springer (2020)","DOI":"10.1007\/978-3-030-58545-7_16"},{"issue":"3","key":"15_CR10","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1109\/JBHI.2020.3002582","volume":"25","author":"K Oh","year":"2020","unstructured":"Oh, K., Oh, I.S., Lee, D.W., et\u00a0al.: Deep anatomical context feature learning for cephalometric landmark detection. IEEE Journal of Biomedical and Health Informatics 25(3), 806\u2013817 (2020)","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","volume":"54","author":"C Payer","year":"2019","unstructured":"Payer, C., \u0160tern, D., Bischof, H., Urschler, M.: Integrating spatial configuration into heatmap regression based cnns for landmark localization. Medical Image Analysis 54, 207\u2013219 (2019)","journal-title":"Medical Image Analysis"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Networks 61, 85\u2013117 (2015)","journal-title":"Neural Networks"},{"issue":"7","key":"15_CR14","doi-asserted-by":"publisher","first-page":"4299","DOI":"10.1007\/s00784-021-03990-w","volume":"25","author":"F Schwendicke","year":"2021","unstructured":"Schwendicke, F., Chaurasia, A., Arsiwala, L., Lee, J.H., Elhennawy, K., Jost-Brinkmann, P.G., Demarco, F., Krois, J.: Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clinical Oral Investigations 25(7), 4299\u20134309 (2021)","journal-title":"Clinical Oral Investigations"},{"key":"15_CR15","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems 30 (2017)"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Song, Y., Qiao, X., Iwamoto, Y., Chen, Y.w.: Automatic cephalometric landmark detection on x-ray images using a deep-learning method. Applied Sciences 10(7), \u00a02547 (2020)","DOI":"10.3390\/app10072547"},{"issue":"5","key":"15_CR17","doi-asserted-by":"publisher","first-page":"812","DOI":"10.2319\/092909-474.1","volume":"80","author":"C Tanikawa","year":"2010","unstructured":"Tanikawa, C., Yamamoto, T., Yagi, M., Takada, K.: Automatic recognition of anatomic features on cephalograms of preadolescent children. The Angle Orthodontist 80(5), 812\u2013820 (2010)","journal-title":"The Angle Orthodontist"},{"issue":"9","key":"15_CR18","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/TMI.2015.2412951","volume":"34","author":"CW Wang","year":"2015","unstructured":"Wang, C.W., Huang, C.T., Hsieh, M.C., Li, C.H., Chang, S.W., Li, W.C., Vandaele, R., Mar\u00e9e, R., Jodogne, S., Geurts, P., et\u00a0al.: Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge. IEEE Transactions on Medical Imaging 34(9), 1890\u20131900 (2015)","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.media.2016.02.004","volume":"31","author":"CW Wang","year":"2016","unstructured":"Wang, C.W., Huang, C.T., Lee, J.H., Li, C.H., Chang, S.W., Siao, M.J., Lai, T.M., Ibragimov, B., Vrtovec, T., Ronneberger, O., et\u00a0al.: A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis 31, 63\u201376 (2016)","journal-title":"Medical Image Analysis"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Wang, C., Chen, Y., Liu, F., Elliott, M., Kwok, C.F., Pe\u00f1a-Solorzano, C., Frazer, H., McCarthy, D.J., Carneiro, G.: An interpretable and accurate deep-learning diagnosis framework modelled with fully and semi-supervised reciprocal learning. IEEE Transactions on Medical Imaging (2023)","DOI":"10.1109\/TMI.2023.3306781"},{"issue":"4","key":"15_CR21","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1109\/TMI.2022.3225667","volume":"42","author":"C Wang","year":"2022","unstructured":"Wang, C., Cui, Z., Yang, J., Han, M., Carneiro, G., Shen, D.: Bowelnet: Joint semantic-geometric ensemble learning for bowel segmentation from both partially and fully labeled ct images. IEEE Transactions on Medical Imaging 42(4), 1225\u20131236 (2022)","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"15_CR22","unstructured":"Wu, Q., Yeo, S.Y., Chen, Y., Liu, J.: Revisiting cephalometric landmark detection from the view of human pose estimation with lightweight super-resolution head. arXiv preprint arXiv:2309.17143 (2023)"},{"issue":"1","key":"15_CR23","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1186\/s12903-023-03452-7","volume":"23","author":"S Yang","year":"2023","unstructured":"Yang, S., Song, E.S., Lee, E.S., Kang, S.R., Yi, W.J., Lee, S.P.: Ceph-net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network. BMC Oral Health 23(1), \u00a0803 (2023)","journal-title":"BMC Oral Health"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Yao, Q., Quan, Q., Xiao, L., Kevin\u00a0Zhou, S.: One-shot medical landmark detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24. pp. 177\u2013188. Springer (2021)","DOI":"10.1007\/978-3-030-87196-3_17"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Yueyuan, A., Hong, W.: Swin transformer combined with convolutional encoder for cephalometric landmarks detection. In: 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). pp. 184\u2013187. IEEE (2021)","DOI":"10.1109\/ICCWAMTIP53232.2021.9674147"},{"key":"15_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101904","volume":"68","author":"M Zeng","year":"2021","unstructured":"Zeng, M., Yan, Z., Liu, S., Zhou, Y., Qiu, L.: Cascaded convolutional networks for automatic cephalometric landmark detection. Medical Image Analysis 68, 101904 (2021)","journal-title":"Medical Image Analysis"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Zhang, Z., Jiao, Z., Gao, X.: An attention-guided deep regression model for landmark detection in cephalograms. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22. pp. 540\u2013548. Springer (2019)","DOI":"10.1007\/978-3-030-32226-7_60"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, T., Wang, W., Konukoglu, E., Van\u00a0Gool, L.: Rethinking semantic segmentation: A prototype view. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2582\u20132593 (2022)","DOI":"10.1109\/CVPR52688.2022.00261"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, H., Yao, Q., Xiao, L., Zhou, S.K.: You only learn once: Universal anatomical landmark detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part V 24. pp. 85\u201395. Springer (2021)","DOI":"10.1007\/978-3-030-87240-3_9"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72086-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:36:35Z","timestamp":1727987795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72086-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720857","9783031720864"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72086-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}