{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:15:20Z","timestamp":1774404920512,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597245","type":"print"},{"value":"9783030597252","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59725-2_69","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T15:02:49Z","timestamp":1601650969000},"page":"712-722","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine\u00a0MRI"],"prefix":"10.1007","author":[{"given":"Rhydian","family":"Windsor","sequence":"first","affiliation":[]},{"given":"Amir","family":"Jamaludin","sequence":"additional","affiliation":[]},{"given":"Timor","family":"Kadir","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Zisserman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"10","key":"69_CR1","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s00586-005-1053-9","volume":"14","author":"M Aebi","year":"2005","unstructured":"Aebi, M.: The adult scoliosis. Eur. Spine J. 14(10), 925\u2013948 (2005)","journal-title":"Eur. Spine J."},{"issue":"8","key":"69_CR2","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1109\/TMI.2015.2392054","volume":"34","author":"Y Cai","year":"2015","unstructured":"Cai, Y., Osman, S., Sharma, M., Landis, M., Li, S.: Multi-modality vertebra recognition in arbitrary views using 3D deformable hierarchical model. IEEE Trans. Med. Imaging 34(8), 1676\u20131693 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"69_CR3","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/s10278-017-9945-x","volume":"30","author":"D Forsberg","year":"2017","unstructured":"Forsberg, D., Sj\u00f6blom, E., Sunshine, J.L.: Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J. Digit. Imaging 30(4), 406\u2013412 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9945-x","journal-title":"J. Digit. Imaging"},{"key":"69_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1007\/978-3-642-33454-2_73","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012","author":"B Glocker","year":"2012","unstructured":"Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 590\u2013598. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33454-2_73"},{"key":"69_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-642-40763-5_33","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"B Glocker","year":"2013","unstructured":"Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 262\u2013270. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_33"},{"key":"69_CR6","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: International Conference on Machine learning, ICML 2006 (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"69_CR7","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning (2017)"},{"key":"69_CR8","unstructured":"Jamaludin, A., Kadir, T., Clark, E., Zisserman, A.: Predicting spine geometry and scoliosis from DXA scans. In: MICCAI Workshop: Computational Methods and Clinical Applications in Musculoskeletal Imaging (2019)"},{"key":"69_CR9","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.media.2017.07.002","volume":"41","author":"A Jamaludin","year":"2017","unstructured":"Jamaludin, A., Kadir, T., Zisserman, A.: SpineNet: automated classification and evidence visualization in spinal MRIs. Med. Image Anal. 41, 63\u201373 (2017)","journal-title":"Med. Image Anal."},{"key":"69_CR10","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1007\/s00586-017-4956-3","volume":"26","author":"A Jamaludin","year":"2017","unstructured":"Jamaludin, A., et al.: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur. Spine J. 26, 1374\u20131383 (2017)","journal-title":"Eur. Spine J."},{"key":"69_CR11","series-title":"Lecture Notes in Computational Vision and Biomechanics","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-3-319-07269-2_19","volume-title":"Computational Methods and Clinical Applications for Spine Imaging","author":"M Lootus","year":"2014","unstructured":"Lootus, M., Kadir, T., Zisserman, A.: Vertebrae detection and labelling in lumbar MR images. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 17, pp. 219\u2013230. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07269-2_19"},{"key":"69_CR12","doi-asserted-by":"crossref","unstructured":"Lootus, M., Kadir, T., Zisserman, A.: Radiological grading of spinal MRI. In: MICCAI Workshop: Computational Methods and Clinical Applications for Spine Imaging (2014)","DOI":"10.1007\/978-3-319-14148-0_11"},{"key":"69_CR13","unstructured":"Lu, J.T., et al.: Deep spine: automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. In: Machine Learning for Healthcare Conference (2018)"},{"issue":"4","key":"69_CR14","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s00264-009-0817-y","volume":"34","author":"C Ozturk","year":"2010","unstructured":"Ozturk, C., Karadereler, S., Ornek, I., Enercan, M., Ganiyusufoglu, K., Hamzaoglu, A.: The role of routine magnetic resonance imaging in the preoperative evaluation of adolescent idiopathic scoliosis. Int. Orthop. 34(4), 543\u2013546 (2010)","journal-title":"Int. Orthop."},{"key":"69_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"69_CR16","doi-asserted-by":"crossref","unstructured":"Scheidl, H., Fiel, S., Sablatnig, R.: Word beam search: a connectionist temporal classification decoding algorithm. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) (2018)","DOI":"10.1109\/ICFHR-2018.2018.00052"},{"issue":"6","key":"69_CR17","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s00223-013-9713-y","volume":"92","author":"HJ Taylor","year":"2013","unstructured":"Taylor, H.J., Harding, I., Hutchinson, J., Nelson, I., Blom, A., Tobias, J.H., Clark, E.M.: Identifying scoliosis in population-based cohorts: development and validation of a novel method based on total-body dual-energy X-ray absorptiometric scans. Calcif. Tissue Int. 92(6), 539\u2013547 (2013)","journal-title":"Calcif. Tissue Int."},{"issue":"2","key":"69_CR18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s13244-016-0468-7","volume":"7","author":"BJ Tins","year":"2016","unstructured":"Tins, B.J., Balain, B.: Incidence of numerical variants and transitional lumbosacral vertebrae on whole-spine MRI. Insights Imaging 7(2), 199\u2013203 (2016). https:\/\/doi.org\/10.1007\/s13244-016-0468-7","journal-title":"Insights Imaging"},{"key":"69_CR19","doi-asserted-by":"crossref","unstructured":"Windsor, R., Jamaludin, A.: The ladder algorithm: finding repetitive structures in medical images by induction. In: IEEE International Symposium on Biomedical Imaging (2020)","DOI":"10.1109\/ISBI45749.2020.9098469"},{"key":"69_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/978-3-319-59050-9_50","volume-title":"Information Processing in Medical Imaging","author":"D Yang","year":"2017","unstructured":"Yang, D., et al.: Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 633\u2013644. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_50"},{"key":"69_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1007\/978-3-030-32251-9_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Zhao","year":"2019","unstructured":"Zhao, S., Wu, X., Chen, B., Li, S.: Automatic vertebrae recognition from arbitrary spine MRI images by a hierarchical self-calibration detection framework. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 316\u2013325. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_35"},{"key":"69_CR22","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. In: arXiv preprint arXiv:1904.07850 (2019)"},{"issue":"6","key":"69_CR23","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1111\/cgf.12343","volume":"33","author":"D Zuki\u0107","year":"2014","unstructured":"Zuki\u0107, D., Vlas\u00e1k, A., Egger, J., Ho\u0159\u00ednek, D., Nimsky, C., Kolb, A.: Robust detection and segmentation for diagnosis of vertebral diseases using routine MR images. Compu. Graph. Forum 33(6), 190\u2013204 (2014)","journal-title":"Compu. Graph. Forum"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59725-2_69","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:09:27Z","timestamp":1759356567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59725-2_69"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597245","9783030597252"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59725-2_69","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"542","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}