{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:17:56Z","timestamp":1770743876095,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164453","type":"print"},{"value":"9783031164460","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16446-0_22","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:02:47Z","timestamp":1663318967000},"page":"227-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Weakly-Supervised Biomechanically-Constrained CT\/MRI Registration of\u00a0the\u00a0Spine"],"prefix":"10.1007","author":[{"given":"Bailiang","family":"Jian","sequence":"first","affiliation":[]},{"given":"Mohammad Farid","family":"Azampour","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"De Benetti","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Oberreuter","sequence":"additional","affiliation":[]},{"given":"Christina","family":"Bukas","sequence":"additional","affiliation":[]},{"given":"Alexandra S.","family":"Gersing","sequence":"additional","affiliation":[]},{"given":"Sarah C.","family":"Foreman","sequence":"additional","affiliation":[]},{"given":"Anna-Sophia","family":"Dietrich","sequence":"additional","affiliation":[]},{"given":"Jon","family":"Rischewski","sequence":"additional","affiliation":[]},{"given":"Jan S.","family":"Kirschke","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Wendler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924\u2013931. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866565_113","DOI":"10.1007\/11866565_113"},{"issue":"8","key":"22_CR2","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: A log-Euclidean framework for statistics on diffeomorphisms. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR3","doi-asserted-by":"publisher","unstructured":"Bukas, C., et al.: Patient-specific virtual spine straightening and vertebra inpainting: an automatic framework for osteoplasty planning. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 529\u2013539. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_51","DOI":"10.1007\/978-3-030-87202-1_51"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"De Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., I\u0161gum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128\u2013143, (2019)","DOI":"10.1016\/j.media.2018.11.010"},{"issue":"1","key":"22_CR5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1002\/mp.14584","volume":"48","author":"Y Fu","year":"2021","unstructured":"Fu, Y., et al.: Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks. Med. Phys. 48(1), 253\u2013263 (2021)","journal-title":"Med. Phys."},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Gill, S., et al.: Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine. Med. Image Anal. 16(3), 662\u2013674 (2012)","DOI":"10.1016\/j.media.2010.07.008"},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1007\/11866763_89","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006","author":"E Haber","year":"2006","unstructured":"Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 726\u2013733. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866763_89"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31(1), 1\u201318 (2020)","DOI":"10.1007\/s00138-020-01060-x"},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Heinrich, M.P., Jenkinson, M., Papie\u017c, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187\u2013194. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40811-3_24","DOI":"10.1007\/978-3-642-40811-3_24"},{"issue":"6","key":"22_CR10","doi-asserted-by":"publisher","first-page":"910","DOI":"10.31616\/asj.2020.0593","volume":"14","author":"GU Kim","year":"2020","unstructured":"Kim, G.U., Chang, M.C., Kim, T.U., Lee, G.W.: Diagnostic modality in spine disease: a review. Asian Spine Journal 14(6), 910 (2020)","journal-title":"Asian Spine Journal"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Kim, J., Matuszak, M.M., Saitou, K., Balter, J.M.: Distance-preserving rigidity penalty on deformable image registration of multiple skeletal components in the neck. Med. Phys. 40(12), (2013)","DOI":"10.1118\/1.4828783"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Little, J.A., Hill, D.L., Hawkes, D.J.: Deformations incorporating rigid structures. Comput. Vis. Image Underst. 66(2), 223\u2013232 (1997)","DOI":"10.1006\/cviu.1997.0608"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"McKenzie, E.M., Santhanam, A., Ruan, D., O\u2019Connor, D., Cao, M., Sheng, K.: Multimodality image registration in the head-and-neck using a deep learning-derived synthetic ct as a bridge. Med. Phys. 47(3), 1094\u20131104 (2020)","DOI":"10.1002\/mp.13976"},{"key":"22_CR14","doi-asserted-by":"publisher","unstructured":"Mok, Tony C. W.., Chung, Albert C. S..: Conditional deformable image registration with convolutional neural network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 35\u201345. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_4","DOI":"10.1007\/978-3-030-87202-1_4"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Momin, S., et al.: CT-MRI pelvic deformable registration via deep learning. In: Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling. vol. 11598, p. 1159818. International Society for Optics and Photonics (2021)","DOI":"10.1117\/12.2581069"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Parizel, P., et al.: Trauma of the spine and spinal cord: imaging strategies. Eur. Spine J. 19(1), 8\u201317 (2010)","DOI":"10.1007\/s00586-009-1123-5"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 452\u2013461. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/978-3-540-40899-4_46","DOI":"10.1007\/978-3-540-40899-4_46"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Rohlfing, T., Maurer, C.R., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22(6), 730\u2013741 (2003)","DOI":"10.1109\/TMI.2003.814791"},{"key":"22_CR19","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Sekuboyina, A., Rempfler, M., Kuka\u010dka, J., Tetteh, G., Valentinitsch, A., Kirschke, J.S., Menze, B.H.: Btrfly Net: vertebrae labelling with energy-based adversarial learning of local spine prior. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 649\u2013657. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_74","DOI":"10.1007\/978-3-030-00937-3_74"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Shah, L.M., Salzman, K.L.: Imaging of spinal metastatic disease. Int. J. Surg. Oncol. 2011 (2011)","DOI":"10.1155\/2011\/769753"},{"key":"22_CR22","unstructured":"Sorkine-Hornung, O., Rabinovich, M.: Least-squares rigid motion using svd (2017)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Staring, M., Klein, S., Pluim, J.P.: A rigidity penalty term for nonrigid registration. Med. Phys. 34(11), 4098\u20134108 (2007)","DOI":"10.1118\/1.2776236"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Tins, B.: Technical aspects of ct imaging of the spine. Insights Imaging 1(5), 349\u2013359 (2010)","DOI":"10.1007\/s13244-010-0047-2"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Wells, W.M., III., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35\u201351 (1996)","DOI":"10.1016\/S1361-8415(01)80004-9"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16446-0_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:05:28Z","timestamp":1721372728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16446-0_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164453","9783031164460"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16446-0_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}