{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:02:24Z","timestamp":1777654944215,"version":"3.51.4"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031469138","type":"print"},{"value":"9783031469145","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46914-5_22","type":"book-chapter","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T07:02:43Z","timestamp":1698649363000},"page":"271-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["3D Shape Analysis of\u00a0Scoliosis"],"prefix":"10.1007","author":[{"given":"Emmanuelle","family":"Bourigault","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Jamaludin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Clark","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy","family":"Fairbank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timor","family":"Kadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Zisserman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"22_CR1","first-page":"990","volume":"19","author":"S Aaro","year":"1978","unstructured":"Aaro, S., Dahlborn, M., Svensson, L.: Estimation of vertebral rotation in structural scoliosis by computer tomography. Acta Radiol. 19, 990\u2013992 (1978)","journal-title":"Acta Radiol."},{"key":"22_CR2","unstructured":"Bourigault, E., Jamaludin, A., Kadir, T., Zisserman, A.: Scoliosis measurement on DXA scans using a combined deep learning and spinal geometry approach. In: Medical Imaging with Deep Learning (2022)"},{"key":"22_CR3","unstructured":"Chen, B., Jiang, J., Wang, X., Wan, P., Wang, J., Long, M.: Debiased self-training for semi-supervised learning (2022). 10.48550\/ARXIV.2202.07136, https:\/\/arxiv.org\/abs\/2202.07136"},{"key":"22_CR4","first-page":"261","volume":"5","author":"J Cobb","year":"1948","unstructured":"Cobb, J.: Outline for the study of scoliosis. Instr. Course Lect. AAOS 5, 261\u2013275 (1948)","journal-title":"Instr. Course Lect. AAOS"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Galbusera, F., Bassani, T., Panico, M., Sconfienza, L.M., Cina, A.: A fresh look at spinal alignment and deformities: automated analysis of a large database of 9832 biplanar radiographs. Front. Bioeng. Biotech. 10, 863054 (2022)","DOI":"10.3389\/fbioe.2022.863054"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Ho, E.K., Upadhyay, S.S., Chan, F.L., Hsu, L.C.S., Leong, J.C.Y.: New methods of measuring vertebral rotation from computed tomographic scans. an intraobserver and interobserver study on girls with scoliosis. Spine 18(9), 1173\u20131777 (1993)","DOI":"10.1097\/00007632-199307000-00008"},{"issue":"2","key":"22_CR7","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.otsr.2018.10.021","volume":"105","author":"TS Ill\u00e9s","year":"2019","unstructured":"Ill\u00e9s, T.S., Lavaste, F., Dubousset, J.: The third dimension of scoliosis: the forgotten axial plane. Orthop. Traumatol. Surg. Res. OTSR 105(2), 351\u2013359 (2019)","journal-title":"Orthop. Traumatol. Surg. Res. OTSR"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00586-010-1566-8","volume":"20","author":"TS Ill\u00e9s","year":"2010","unstructured":"Ill\u00e9s, T.S., Tunyogi-Csap\u00f3, M., Somoske\u00f6y, S.: Breakthrough in three-dimensional scoliosis diagnosis: significance of horizontal plane view and vertebra vectors. Eur. Spine J. 20, 135\u2013143 (2010)","journal-title":"Eur. Spine J."},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Jamaludin, A., Kadir, T., Clark, E., Zisserman, A.: Predicting scoliosis in DXA scans using intermediate representations. In: MICCAI Workshop: MSKI (2018)","DOI":"10.1007\/978-3-030-13736-6_2"},{"key":"22_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-41827-8_9","volume-title":"Computational Methods and Clinical Applications for Spine Imaging","author":"A Jamaludin","year":"2016","unstructured":"Jamaludin, A., Lootus, M., Kadir, T., Zisserman, A.: Automatic intervertebral discs localization and segmentation: a vertebral approach. In: Vrtovec, T., et al. (eds.) CSI 2015. LNCS, vol. 9402, pp. 97\u2013103. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41827-8_9"},{"key":"22_CR11","doi-asserted-by":"publisher","unstructured":"Karam, M., et al.: Global malalignment in adolescent idiopathic scoliosis: the axial deformity is the main driver. Eur. Spine J. 1\u201313 (2022). https:\/\/doi.org\/10.1007\/s00586-021-07101-x","DOI":"10.1007\/s00586-021-07101-x"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/s41597-022-01222-8","volume":"9","author":"YA Khalil","year":"2022","unstructured":"Khalil, Y.A., et al.: Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database. Sci. Data 9, 97 (2022)","journal-title":"Sci. Data"},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11832-012-0457-4","volume":"7","author":"MR Konieczny","year":"2013","unstructured":"Konieczny, M.R., Senyurt, H., Krauspe, R.: Epidemiology of adolescent idiopathic scoliosis. J. Child. Orthop. 7, 3\u20139 (2013)","journal-title":"J. Child. Orthop."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Ma, Q., et al.: Coronal balance vs. sagittal profile in adolescent idiopathic scoliosis, are they correlated? Front. Pediatr. 7 (2020)","DOI":"10.3389\/fped.2019.00523"},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"Pasha, S.: Data-driven classification of the 3d spinal curve in adolescent idiopathic scoliosis with an applications in surgical outcome prediction. Sci. Rep. (2018). https:\/\/doi.org\/10.1038\/s41598-018-34261-6","DOI":"10.1038\/s41598-018-34261-6"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Pasha, S., Ecker, M., Ho, V., Hassanzadeh, P.: A hierarchical classification of adolescent idiopathic scoliosis: Identifying the distinguishing features in 3d spinal deformities. PLoS ONE (2019). https:\/\/doi.org\/10.1371\/journal.pone.0213406","DOI":"10.1371\/journal.pone.0213406"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Roaf, R.: Rotation movements of the spine with special reference to scoliosis. J. Bone Joint Surgery. Br. 40-B(2), 312\u2013332 (1958)","DOI":"10.1302\/0301-620X.40B2.312"},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Rockenfeller, R., M\u00fcller, A.: Augmenting the cobb angle: three-dimensional analysis of whole spine shapes using b\u00e9zier curves. Comput. Methods Programs Biomed. 225, 107075 (2022). https:\/\/doi.org\/10.1016\/j.cmpb.2022.107075","DOI":"10.1016\/j.cmpb.2022.107075"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Ruppert, D., Wand, M.P., Carroll, R.J.: Semiparametric Regression. No. 12, Cambridge University Press, Cambridge (2003)","DOI":"10.1017\/CBO9780511755453"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Sudlow, C.L.M., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015)","DOI":"10.1371\/journal.pmed.1001779"},{"key":"22_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s00223-013-9713-y","volume":"92","author":"H Taylor","year":"2013","unstructured":"Taylor, H., et al.: 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, 539\u2013547 (2013)","journal-title":"Calcif. Tissue Int."},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Windsor, R., Jamaludin, A., Kadir, T., Zisserman, A.: A convolutional approach to vertebrae detection and labelling in whole spine MRI. In: MICCAI (2020)","DOI":"10.1007\/978-3-030-59725-2_69"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Yi-de, M., Qing, L., Zhi-bai, Q.: Automated image segmentation using improved PCNN model based on cross-entropy. In: Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, vol. 2004, pp. 743\u2013746 (2004)","DOI":"10.1109\/ISIMP.2004.1434171"},{"key":"22_CR26","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15, 749\u2013753 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"22_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46914-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T01:03:50Z","timestamp":1730423030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46914-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031469138","9783031469145"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46914-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","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":"23","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":"85% - 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":"2","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}