{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T22:40:10Z","timestamp":1759617610174,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030603649"},{"type":"electronic","value":"9783030603656"}],"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\/978-3-030-60365-6_1","type":"book-chapter","created":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T06:05:01Z","timestamp":1601877901000},"page":"3-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Image Registration via Stochastic Gradient Markov Chain Monte Carlo"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Grzech","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Glocker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lo\u00efc","family":"le Folgoc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,5]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/11866565_113","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006","author":"V Arsigny","year":"2006","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"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00964"},{"issue":"8","key":"1_CR3","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":"1_CR4","unstructured":"Besag, J.: Comments on \u201crepresentations of knowledge in complex systems\u201d by U. Grenander and MI Miller. J. R. Stat. Soc. 56, 591\u2013592 (1993)"},{"key":"1_CR5","unstructured":"Chen, C., Carlson, D., Gan, Z., Li, C., Carin, L.: Bridging the gap between stochastic gradient MCMC and stochastic optimization. In: AISTATS (2016)"},{"key":"1_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/978-3-030-00928-1_82","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AV Dalca","year":"2018","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 729\u2013738. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_82"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. MedIA Med. Image Anal. 57, 226\u2013236 (2019)","DOI":"10.1016\/j.media.2019.07.006"},{"issue":"6","key":"1_CR8","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1016\/j.media.2008.03.006","volume":"12","author":"B Glocker","year":"2008","unstructured":"Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731\u2013741 (2008)","journal-title":"Med. Image Anal."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1146\/annurev-bioeng-071910-124649","volume":"13","author":"B Glocker","year":"2011","unstructured":"Glocker, B., Sotiras, A., Komodakis, N., Paragios, N.: Deformable medical image registration: setting the state of the art with discrete methods. Annu. Rev. Biomed. Eng. 13, 219\u2013244 (2011)","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"9","key":"1_CR10","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1109\/TIP.2012.2200495","volume":"21","author":"M Hachama","year":"2012","unstructured":"Hachama, M., Desolneux, A., Richard, F.J.: Bayesian technique for image classifying registration. IEEE Trans. Image Process. 21(9), 4080\u20134091 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/TMI.2013.2246577","volume":"32","author":"HP Heinrich","year":"2013","unstructured":"Heinrich, H.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239\u20131248 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/978-3-642-31340-0_6","volume-title":"Biomedical Image Registration","author":"F Janoos","year":"2012","unstructured":"Janoos, F., Risholm, P., Wells, W.: Bayesian characterization of uncertainty in multi-modal image registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 50\u201359. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-31340-0_6"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Karabulut, N., Erdil, E., \u00c7etin, M.: A Markov chain Monte Carlo based rigid image registration method. Technical report (2017)","DOI":"10.1109\/SIU.2017.7960520"},{"issue":"1","key":"1_CR14","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2009","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196\u2013205 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"1_CR15","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/TMI.2016.2623608","volume":"36","author":"L Le Folgoc","year":"2017","unstructured":"Le Folgoc, L., Delingette, H., Criminisi, A., Ayache, N.: Quantifying registration uncertainty with sparse Bayesian modelling. IEEE Trans. Med. Imaging 36(2), 607\u2013617 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/978-3-030-32245-8_38","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"MCH Lee","year":"2019","unstructured":"Lee, M.C.H., Oktay, O., Schuh, A., Schaap, M., Glocker, B.: Image-and-spatial transformer networks for structure-guided image registration. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 337\u2013345. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_38"},{"key":"1_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-030-32245-8_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Luo","year":"2019","unstructured":"Luo, J., Sedghi, A., Popuri, K., Cobzas, D., Zhang, M., Preiswerk, F., Toews, M., Golby, A., Sugiyama, M., Wells, W.M., Frisken, S.: On the applicability of registration uncertainty. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 410\u2013419. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_46"},{"key":"1_CR18","first-page":"1","volume":"18","author":"S Mandt","year":"2017","unstructured":"Mandt, S., Hoffman, M.D., Blei, D.M.: Stochastic gradient descent as approximate Bayesian inference. J. Mach. Learn. Res. 18, 1\u201335 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR19","series-title":"LNCS","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0092831","volume-title":"Sobolev Gradients and Differential Equations","author":"JW Neuberger","year":"1997","unstructured":"Neuberger, J.W., Dold, A., Takens, F.: Sobolev Gradients and Differential Equations. LNCS. Springer, Heidelberg (1997)"},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.media.2013.03.002","volume":"17","author":"P Risholm","year":"2013","unstructured":"Risholm, P., Janoos, F., Norton, I., Golby, A.J., Wells, W.M.: Bayesian characterization of uncertainty in intra-subject non-rigid registration. Med. Image Anal. 17, 538\u2013555 (2013)","journal-title":"Med. Image Anal."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Schultz, S., Handels, H., Ehrhardt, J.: A multilevel Markov chain Monte Carlo approach for uncertainty quantification in deformable registration. In: SPIE Medical Imaging (2018)","DOI":"10.1117\/12.2293588"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Schultz, S., Kr\u00fcger, J., Handels, H., Ehrhardt, J.: Bayesian inference for uncertainty quantification in point-based deformable image registration. In: SPIE Medical Imaging, p. 46, March 2019","DOI":"10.1117\/12.2512988"},{"issue":"3","key":"1_CR23","doi-asserted-by":"publisher","first-page":"2438","DOI":"10.1016\/j.neuroimage.2011.09.002","volume":"59","author":"IJ Simpson","year":"2012","unstructured":"Simpson, I.J., Schnabel, J.A., Groves, A.R., Andersson, J.L., Woolrich, M.W.: Probabilistic inference of regularisation in non-rigid registration. Neuroimage 59(3), 2438\u20132451 (2012)","journal-title":"Neuroimage"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Slavcheva, M., Baust, M., Ilic, S.: SobolevFusion: 3D reconstruction of scenes undergoing free non-rigid motion. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00280"},{"key":"1_CR25","unstructured":"Welling, M., Teh, Y.W.: Bayesian learning via stochastic gradient Langevin dynamics. In: ICML, pp. 681\u2013688 (2011)"},{"key":"1_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-319-10443-0_16","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"M Zhang","year":"2014","unstructured":"Zhang, M., Fletcher, P.T.: Bayesian principal geodesic analysis in diffeomorphic image registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 121\u2013128. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10443-0_16"},{"key":"1_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-642-38868-2_4","volume-title":"Information Processing in Medical Imaging","author":"M Zhang","year":"2013","unstructured":"Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Z\u00f6llei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37\u201348. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38868-2_4"},{"key":"1_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1007\/978-3-540-73273-0_55","volume-title":"Information Processing in Medical Imaging","author":"L Z\u00f6llei","year":"2007","unstructured":"Z\u00f6llei, L., Jenkinson, M., Timoner, S., Wells, W.: A marginalized MAP approach and EM optimization for pair-wise registration. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 662\u2013674. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73273-0_55"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60365-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T22:02:28Z","timestamp":1759615348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60365-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030603649","9783030603656"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60365-6_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","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":"8 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":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io\/","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":"18","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":"10","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":"56% - 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":"2","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 workshop 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)"}}]}}