{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:41:47Z","timestamp":1770277307686,"version":"3.49.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597153","type":"print"},{"value":"9783030597160","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\/978-3-030-59716-0_25","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T20:03:41Z","timestamp":1601669021000},"page":"253-263","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Flexible Bayesian Modelling for Nonlinear Image Registration"],"prefix":"10.1007","author":[{"given":"Mikael","family":"Brudfors","sequence":"first","affiliation":[]},{"given":"Ya\u00ebl","family":"Balbastre","sequence":"additional","affiliation":[]},{"given":"Guillaume","family":"Flandin","sequence":"additional","affiliation":[]},{"given":"Parashkev","family":"Nachev","sequence":"additional","affiliation":[]},{"given":"John","family":"Ashburner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"3","key":"25_CR1","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1002\/hbm.460030303","volume":"3","author":"KJ Friston","year":"1995","unstructured":"Friston, K.J., Ashburner, J., Frith, C.D., Poline, J.-B., Heather, J.D., Frackowiak, R.S.: Spatial registration and normalization of images. Hum. Brain Mapp. 3(3), 165\u2013189 (1995)","journal-title":"Hum. Brain Mapp."},{"key":"25_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/11569541_30","volume-title":"Computer Vision for Biomedical Image Applications","author":"L Z\u00f6llei","year":"2005","unstructured":"Z\u00f6llei, L., Learned-Miller, E., Grimson, E., Wells, W.: Efficient population registration of 3D data. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 291\u2013301. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11569541_30"},{"issue":"1","key":"25_CR3","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neuroimage.2010.01.072","volume":"51","author":"RA Heckemann","year":"2010","unstructured":"Heckemann, R.A., et al.: Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage 51(1), 221\u2013227 (2010)","journal-title":"Neuroimage"},{"issue":"6972","key":"25_CR4","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1038\/427311a","volume":"427","author":"B Draganski","year":"2004","unstructured":"Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., May, A.: Changes in grey matter induced by training. Nature 427(6972), 311\u2013312 (2004)","journal-title":"Nature"},{"issue":"3","key":"25_CR5","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1002\/hbm.460030302","volume":"3","author":"PT Fox","year":"1995","unstructured":"Fox, P.T.: Spatial normalization origins: objectives, applications, and alternatives. Hum. Brain Mapp. 3(3), 161\u2013164 (1995)","journal-title":"Hum. Brain Mapp."},{"issue":"19","key":"25_CR6","doi-asserted-by":"publisher","first-page":"11406","DOI":"10.1073\/pnas.95.19.11406","volume":"95","author":"JG Csernansky","year":"1998","unstructured":"Csernansky, J.G., et al.: Hippocampal morphometry in schizophrenia by high dimensional brain mapping. PNAS 95(19), 11406\u201311411 (1998)","journal-title":"PNAS"},{"issue":"5","key":"25_CR7","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1017\/S0033291711002005","volume":"42","author":"J Mourao-Miranda","year":"2012","unstructured":"Mourao-Miranda, J., et al.: Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychol. Med. 42(5), 1037\u20131047 (2012)","journal-title":"Psychol. Med."},{"issue":"4","key":"25_CR8","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1016\/j.neuroimage.2008.03.028","volume":"41","author":"ML Seghier","year":"2008","unstructured":"Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage 41(4), 1253\u20131266 (2008)","journal-title":"NeuroImage"},{"issue":"8","key":"25_CR9","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/nmeth.1635","volume":"8","author":"T Yarkoni","year":"2011","unstructured":"Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8(8), 665 (2011)","journal-title":"Nat. Methods"},{"issue":"6","key":"25_CR10","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/42.650882","volume":"16","author":"GE Christensen","year":"1997","unstructured":"Christensen, G.E., Joshi, S.C., Miller, M.I.: Volumetric transformation of brain anatomy. IEEE Trans. Med. Imaging 16(6), 864\u2013877 (1997)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"25_CR11","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"issue":"3","key":"25_CR12","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26(3), 839\u2013851 (2005)","journal-title":"NeuroImage"},{"issue":"1","key":"25_CR13","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95\u2013113 (2007)","journal-title":"NeuroImage"},{"key":"25_CR14","unstructured":"Andersson, J.L., Jenkinson, M., Smith, S., et al.: \u201cNon-linear registration aka spatial normalisation FMRIB Technical report TR07JA2,\u201d FMRIB Analysis Group of the University of Oxford (2007)"},{"key":"25_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-3-540-75757-3_65","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2007","author":"KK Bhatia","year":"2007","unstructured":"Bhatia, K.K., et al.: Groupwise combined segmentation and registration for atlas construction. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 532\u2013540. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-75757-3_65"},{"issue":"1","key":"25_CR16","doi-asserted-by":"publisher","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","volume":"45","author":"T Vercauteren","year":"2009","unstructured":"Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61\u2013S72 (2009)","journal-title":"NeuroImage"},{"issue":"8","key":"25_CR17","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.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"25_CR18","unstructured":"Dalca, A., Rakic, M., Guttag, J., Sabuncu, M.: Learning conditional deformable templates with convolutional networks. In: NeurIPS, pp. 804\u2013816 (2019)"},{"key":"25_CR19","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.media.2019.03.006","volume":"54","author":"J Fan","year":"2019","unstructured":"Fan, J., Cao, X., Yap, P.-T., Shen, D.: BIRNet: brain image registration using dual-supervised fully convolutional networks. Med. Image Anal. 54, 193\u2013206 (2019)","journal-title":"Med. Image Anal."},{"issue":"9","key":"25_CR20","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1109\/TMI.2019.2897112","volume":"38","author":"J Krebs","year":"2019","unstructured":"Krebs, J., Delingette, H., Mailh\u00e9, B., Ayache, N., Mansi, T.: Learning a probabilistic model for diffeomorphic registration. IEEE Trans. Med. Imaging 38(9), 2165\u20132176 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Beg, M.F., Khan, A.: Computing an average anatomical atlas using LDDMM and geodesic shooting. In: ISBI, pp. 1116\u20131119, IEEE (2006)","DOI":"10.1109\/ISBI.2006.1625118"},{"key":"25_CR22","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"key":"25_CR23","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neuroimage.2017.10.060","volume":"166","author":"C Blaiotta","year":"2018","unstructured":"Blaiotta, C., Freund, P., Cardoso, M.J., Ashburner, J.: Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage 166, 117\u2013134 (2018)","journal-title":"NeuroImage"},{"key":"25_CR24","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.04.008","volume":"55","author":"J Ashburner","year":"2019","unstructured":"Ashburner, J., Brudfors, M., Bronik, K., Balbastre, Y.: An algorithm for learning shape and appearance models without annotations. Med. Image Anal. 55, 197 (2019)","journal-title":"Med. Image Anal."},{"issue":"2","key":"25_CR25","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s10851-005-3624-0","volume":"24","author":"MI Miller","year":"2006","unstructured":"Miller, M.I., Trouv\u00e9, A., Younes, L.: Geodesic shooting for computational anatomy. J. Math. Imaging Vis. 24(2), 209\u2013228 (2006)","journal-title":"J. Math. Imaging Vis."},{"issue":"3","key":"25_CR26","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/S1053-8119(03)00019-3","volume":"18","author":"RP Woods","year":"2003","unstructured":"Woods, R.P.: Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation. NeuroImage 18(3), 769\u2013788 (2003)","journal-title":"NeuroImage"},{"issue":"2","key":"25_CR27","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neuroimage.2008.12.008","volume":"45","author":"J Ashburner","year":"2009","unstructured":"Ashburner, J., Friston, K.J.: Computing average shaped tissue probability templates. NeuroImage 45(2), 333\u2013341 (2009)","journal-title":"NeuroImage"},{"issue":"1","key":"25_CR28","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/BF00048682","volume":"44","author":"D B\u00f6hning","year":"1992","unstructured":"B\u00f6hning, D.: Multinomial logistic regression algorithm. Ann. Inst. Stat. Math. 44(1), 197\u2013200 (1992)","journal-title":"Ann. Inst. Stat. Math."},{"issue":"3","key":"25_CR29","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","volume":"46","author":"A Klein","year":"2009","unstructured":"Klein, A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786\u2013802 (2009)","journal-title":"NeuroImage"},{"issue":"1","key":"25_CR30","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jneumeth.2004.07.014","volume":"142","author":"BA Ardekani","year":"2005","unstructured":"Ardekani, B.A., Guckemus, S., Bachman, A., Hoptman, M.J., Wojtaszek, M., Nierenberg, J.: Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J. Neurosci. Methods 142(1), 67\u201376 (2005)","journal-title":"J. Neurosci. Methods"},{"issue":"3","key":"25_CR31","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/j.neuroimage.2010.12.049","volume":"55","author":"J Ashburner","year":"2011","unstructured":"Ashburner, J., Friston, K.J.: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55(3), 954\u2013967 (2011)","journal-title":"NeuroImage"},{"issue":"2","key":"25_CR32","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2), 825\u2013841 (2002)","journal-title":"NeuroImage"},{"key":"25_CR33","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.neuroimage.2014.09.034","volume":"104","author":"IB Malone","year":"2015","unstructured":"Malone, I.B., et al.: Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. NeuroImage 104, 366\u2013372 (2015)","journal-title":"NeuroImage"},{"key":"25_CR34","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-319-95921-4_7","volume-title":"Medical Image Understanding and Analysis","author":"G Ridgway","year":"2018","unstructured":"Ridgway, G., et al.: Voxel-Wise analysis of paediatric liver MRI. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds.) MIUA 2018. CCIS, vol. 894, pp. 57\u201362. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95921-4_7"},{"key":"25_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-32778-1_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"M Brudfors","year":"2019","unstructured":"Brudfors, M., Ashburner, J., Nachev, P., Balbastre, Y.: Empirical bayesian mixture models for medical image translation. In: Burgos, N., Gooya, A., Svoboda, D. (eds.) SASHIMI 2019. LNCS, vol. 11827, pp. 1\u201312. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32778-1_1"}],"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\/978-3-030-59716-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:03:27Z","timestamp":1759442607000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59716-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597153","9783030597160"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59716-0_25","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)"}}]}}