{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T12:46:15Z","timestamp":1773751575242,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872304","type":"print"},{"value":"9783030872311","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87231-1_44","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T15:05:11Z","timestamp":1632323111000},"page":"451-460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Revisiting Contour-Driven and Knowledge-Based Deformable Models: Application to 2D-3D Proximal Femur Reconstruction from X-ray Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8784-1467","authenticated-orcid":false,"given":"Christophe","family":"Ch\u00eanes","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2464-8971","authenticated-orcid":false,"given":"J\u00e9r\u00f4me","family":"Schmid","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.bone.2013.12.006","volume":"60","author":"N Sarkalkan","year":"2014","unstructured":"Sarkalkan, N., Weinans, H., Zadpoor, A.A.: Statistical shape and appearance models of bones. Bone 60, 129\u2013140 (2014). https:\/\/doi.org\/10.1016\/j.bone.2013.12.006","journal-title":"Bone"},{"issue":"6","key":"44_CR2","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.compmedimag.2010.09.008","volume":"35","author":"P Gamage","year":"2011","unstructured":"Gamage, P., Xie, S.Q., Delmas, P., Xu, W.L.: Diagnostic radiograph based 3D bone reconstruction framework: application to the femur. Comput. Med. Imaging Graph. 35(6), 427\u2013437 (2011). https:\/\/doi.org\/10.1016\/j.compmedimag.2010.09.008","journal-title":"Comput. Med. Imaging Graph."},{"issue":"10","key":"44_CR3","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/j.medengphy.2010.08.009","volume":"32","author":"PE Galibarov","year":"2010","unstructured":"Galibarov, P.E., Prendergast, P.J., Lennon, A.B.: A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs. Med. Eng. Phys. 32(10), 1180\u20131188 (2010). https:\/\/doi.org\/10.1016\/j.medengphy.2010.08.009","journal-title":"Med. Eng. Phys."},{"issue":"3","key":"44_CR4","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1016\/j.media.2010.03.005","volume":"16","author":"P Markelj","year":"2012","unstructured":"Markelj, P., Toma\u017eevi\u010d, D., Likar, B., Pernu\u0161, F.: A review of 3D\/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642\u2013661 (2012). https:\/\/doi.org\/10.1016\/j.media.2010.03.005","journal-title":"Med. Image Anal."},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/RBME.2018.2876450","volume":"12","author":"CJF Reyneke","year":"2019","unstructured":"Reyneke, C.J.F., L\u00fcthi, M., Burdin, V., Douglas, T.S., Vetter, T., Mutsvangwa, T.E.M.: Review of 2-D\/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: toward a unified framework. IEEE Rev. Biomed. Eng. 12, 269\u2013286 (2019). https:\/\/doi.org\/10.1109\/RBME.2018.2876450","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"4","key":"44_CR6","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/s11548-014-1097-6","volume":"10","author":"V Karade","year":"2014","unstructured":"Karade, V., Ravi, B.: 3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation. Int. J. Comput. Assist. Radiol. Surg. 10(4), 473\u2013485 (2014). https:\/\/doi.org\/10.1007\/s11548-014-1097-6","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Zheng, G., Yu, W.: Chapter 12 - Statistical shape and deformation models based 2D\u20133D reconstruction. In: Zheng, G., Li, S., Sz\u00e9kely, G. (eds.) Statistical Shape and Deformation Analysis, pp. 329\u2013349. Academic Press (2017)","DOI":"10.1016\/B978-0-12-810493-4.00015-8"},{"key":"44_CR8","doi-asserted-by":"publisher","unstructured":"Klima, O., Kleparnik, P., Spanel, M., Zemcik, P.: Intensity-based femoral atlas 2D\/3D registration using Levenberg-Marquardt optimization. In: Medical Imaging 2016: Biomedical Applications in Molecular Structural, and Functional Imaging, vol. 9788, p. 97880F (2016)https:\/\/doi.org\/10.1117\/12.2216529","DOI":"10.1117\/12.2216529"},{"issue":"9","key":"44_CR9","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1007\/s11548-016-1400-9","volume":"11","author":"W Yu","year":"2016","unstructured":"Yu, W., Chu, C., Tannast, M., Zheng, G.: Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images. Int. J. Comput. Assist. Radiol. Surg. 11(9), 1673\u20131685 (2016). https:\/\/doi.org\/10.1007\/s11548-016-1400-9","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"6","key":"44_CR10","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1016\/j.media.2011.04.001","volume":"15","author":"N Baka","year":"2011","unstructured":"Baka, N., et al.: 2D\u20133D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med. Image Anal. 15(6), 840\u2013850 (2011). https:\/\/doi.org\/10.1016\/j.media.2011.04.001","journal-title":"Med. Image Anal."},{"key":"44_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/978-3-642-23629-7_73","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2011","author":"G Zheng","year":"2011","unstructured":"Zheng, G.: Personalized X-Ray reconstruction of the proximal femur via intensity-based non-rigid 2D-3D registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 598\u2013606. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23629-7_73"},{"key":"44_CR12","doi-asserted-by":"publisher","unstructured":"Boussaid, H., Kadoury, S., Kokkinos, I., Lazennec, J.-Y., Zheng, G., Paragios, N.: 3D model-based reconstruction of the proximal femur from low-dose Biplanar X-Ray images. In: The 22nd British Machine Vision Conference - BMVC 2011, Dundee, United Kingdom, pp. 1\u201310, August 2011. https:\/\/doi.org\/10.5244\/C.25.35","DOI":"10.5244\/C.25.35"},{"issue":"4","key":"44_CR13","doi-asserted-by":"publisher","first-page":"e1823","DOI":"10.1002\/rcs.1823","volume":"13","author":"P Cerveri","year":"2017","unstructured":"Cerveri, P., Sacco, C., Olgiati, G., Manzotti, A., Baroni, G.: 2D\/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis. Int. J. Med. Robot. Comput. Assisted Surgery 13(4), e1823 (2017). https:\/\/doi.org\/10.1002\/rcs.1823","journal-title":"Int. J. Med. Robot. Comput. Assisted Surgery"},{"key":"44_CR14","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.medengphy.2017.08.016","volume":"50","author":"K Youn","year":"2017","unstructured":"Youn, K., Park, M.S., Lee, J.: Iterative approach for 3D reconstruction of the femur from un-calibrated 2D radiographic images. Med. Eng. Phys. 50, 89\u201395 (2017). https:\/\/doi.org\/10.1016\/j.medengphy.2017.08.016","journal-title":"Med. Eng. Phys."},{"issue":"1","key":"44_CR15","doi-asserted-by":"publisher","first-page":"016001","DOI":"10.1117\/1.JMI.8.1.016001","volume":"8","author":"J Wu","year":"2021","unstructured":"Wu, J., Mahfouz, M.R.: Reconstruction of knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model. JMI 8(1), 016001 (2021). https:\/\/doi.org\/10.1117\/1.JMI.8.1.016001","journal-title":"JMI"},{"issue":"2","key":"44_CR16","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.jbiomech.2004.02.025","volume":"38","author":"MR Mahfouz","year":"2005","unstructured":"Mahfouz, M.R., Hoff, W.A., Komistek, R.D., Dennis, D.A.: Effect of segmentation errors on 3D-to-2D registration of implant models in X-ray images. J. Biomech. 38(2), 229\u2013239 (2005). https:\/\/doi.org\/10.1016\/j.jbiomech.2004.02.025","journal-title":"J. Biomech."},{"issue":"5","key":"44_CR17","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s11548-020-02162-7","volume":"15","author":"RB Grupp","year":"2020","unstructured":"Grupp, R.B., et al.: Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D\/3D registration. Int. J. Comput. Assist. Radiol. Surg. 15(5), 759\u2013769 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02162-7","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"44_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-030-61598-7_12","volume-title":"Machine Learning for Medical Image Reconstruction","author":"Y Kasten","year":"2020","unstructured":"Kasten, Y., Doktofsky, D., Kovler, I.: End-To-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images. In: Deeba, F., Johnson, P., W\u00fcrfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 123\u2013133. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61598-7_12"},{"issue":"11","key":"44_CR19","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TMI.2003.819288","volume":"22","author":"H Livyatan","year":"2003","unstructured":"Livyatan, H., Yaniv, Z., Joskowicz, L.: Gradient-based 2-D\/3-D rigid registration of fluoroscopic X-ray to CT. IEEE Trans. Med. Imaging 22(11), 1395\u20131406 (2003). https:\/\/doi.org\/10.1109\/TMI.2003.819288","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"44_CR20","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s11548-018-1899-z","volume":"14","author":"D Damopoulos","year":"2019","unstructured":"Damopoulos, D., et al.: Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration. Int. J. Comput. Assist. Radiol. Surg. 14(3), 545\u2013561 (2019). https:\/\/doi.org\/10.1007\/s11548-018-1899-z","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"6","key":"44_CR21","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2959609","journal-title":"IEEE Trans. Med. Imaging"},{"key":"44_CR22","doi-asserted-by":"publisher","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollar, P.: Designing network design spaces. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 10425\u201310433, June. 2020. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01044","DOI":"10.1109\/CVPR42600.2020.01044"},{"issue":"5","key":"44_CR23","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/S0895-6111(03)00019-3","volume":"27","author":"S Benameur","year":"2003","unstructured":"Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., de Guise, J.: 3D\/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput. Med. Imaging Graph. 27(5), 321\u2013337 (2003). https:\/\/doi.org\/10.1016\/S0895-6111(03)00019-3","journal-title":"Comput. Med. Imaging Graph."},{"key":"44_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-00937-3_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Unberath","year":"2018","unstructured":"Unberath, M., et al.: DeepDRR \u2013 a catalyst for machine learning in fluoroscopy-guided procedures. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 98\u2013106. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_12"},{"key":"44_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58595-2_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"R Hataya","year":"2020","unstructured":"Hataya, R., Zdenek, J., Yoshizoe, K., Nakayama, H.: Faster AutoAugment: learning augmentation strategies using Backpropagation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 1\u201316. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_1"},{"issue":"2","key":"44_CR26","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.cviu.2008.08.012","volume":"113","author":"R Kurazume","year":"2009","unstructured":"Kurazume, R., et al.: 3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images. Comput. Vis. Image Underst. 113(2), 202\u2013211 (2009). https:\/\/doi.org\/10.1016\/j.cviu.2008.08.012","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87231-1_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T20:06:12Z","timestamp":1698955572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.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":"1622","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":"531","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":"33% - 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.","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)"}}]}}