{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:56:11Z","timestamp":1773320171441,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"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_19","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T20:03:41Z","timestamp":1601669021000},"page":"190-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7489-1972","authenticated-orcid":false,"given":"Mattias P.","family":"Heinrich","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3963-7052","authenticated-orcid":false,"given":"Lasse","family":"Hansen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"19_CR1","unstructured":"Anonymous: Tackling the problem of large deformations in deep learning based medical image registration using displacement embeddings. Medical Imaging with Deep Learning, pp. 1\u20135 (2020, under reviewed). https:\/\/openreview.net\/pdf?id=kPBUZluVq"},{"key":"19_CR2","doi-asserted-by":"crossref","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. Imag. (2019)","DOI":"10.1109\/TMI.2019.2897538"},{"key":"19_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-030-32226-7_72","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Blendowski","year":"2019","unstructured":"Blendowski, M., Nickisch, H., Heinrich, M.P.: How to learn from unlabeled volume data: self-supervised 3D context feature\u00a0learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 649\u2013657. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_72"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/978-3-658-25326-4_67","volume-title":"BVM","author":"D Budelmann","year":"2019","unstructured":"Budelmann, D., K\u00f6nig, L., Papenberg, N., Lellmann, J.: Fully-deformable 3D image registration in two seconds. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds.) BVM, pp. 302\u2013307. Springer, Heidelberg (2019). https:\/\/doi.org\/10.1007\/978-3-658-25326-4_67"},{"key":"19_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-030-11726-9_32","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"W Chen","year":"2019","unstructured":"Chen, W., Liu, B., Peng, S., Sun, J., Qiao, X.: S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 358\u2013368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_32"},{"key":"19_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":"19_CR7","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: Proceedings of ICCV, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Eppenhof, K.A., Lafarge, M.W., Veta, M., Pluim, J.P.: Progressively trained convolutional neural networks for deformable image registration. IEEE Trans. Med. Imag. (2019)","DOI":"10.1117\/12.2512428"},{"issue":"5","key":"19_CR9","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1109\/TMI.2018.2878316","volume":"38","author":"KA Eppenhof","year":"2018","unstructured":"Eppenhof, K.A., Pluim, J.P.: Pulmonary CT registration through supervised learning with convolutional neural networks. IEEE Trans. Med. Imag. 38(5), 1097\u20131105 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"19_CR10","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":"19_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/978-3-030-32226-7_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"MP Heinrich","year":"2019","unstructured":"Heinrich, M.P.: Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 50\u201358. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_6"},{"issue":"7","key":"19_CR12","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/TMI.2013.2246577","volume":"32","author":"MP Heinrich","year":"2013","unstructured":"Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imag. 32(7), 1239\u20131248 (2013)","journal-title":"IEEE Trans. Med. Imag."},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2019.02.006","volume":"54","author":"MP Heinrich","year":"2019","unstructured":"Heinrich, M.P., Oktay, O., Bouteldja, N.: OBELISK-Net: fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions. Med. Image Anal. 54, 1\u20139 (2019)","journal-title":"Med. Image Anal."},{"key":"19_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-642-40811-3_24","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"MP Heinrich","year":"2013","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"},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/978-3-030-32226-7_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"A Hering","year":"2019","unstructured":"Hering, A., van Ginneken, B., Heldmann, S.: mlVIRNET: multilevel variational image registration network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 257\u2013265. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_29"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Hering, A., Kuckertz, S., Heldmann, S., Heinrich, M.P.: Memory-efficient 2.5 D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans. Int. J. Comput. Assist. Radiol. Surg. 14(11), 1901\u20131912 (2019)","DOI":"10.1007\/s11548-019-02068-z"},{"issue":"9","key":"19_CR17","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. Imag. 38(9), 2165\u20132176 (2019)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"19_CR18","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","volume":"33","author":"C Liu","year":"2010","unstructured":"Liu, C., Yuen, J., Torralba, A.: SIFT flow: dense correspondence across scenes and its applications. IEEE Trans. Patt. Anal. Mach. Intell. 33(5), 978\u2013994 (2010)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"19_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"3","key":"19_CR20","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.cmpb.2009.09.002","volume":"98","author":"M Modat","year":"2010","unstructured":"Modat, M., et al.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98(3), 278\u2013284 (2010)","journal-title":"Comput. Methods Programs Biomed."},{"key":"19_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","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"},{"issue":"8","key":"19_CR22","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TMI.2017.2691259","volume":"36","author":"J R\u00fchaak","year":"2017","unstructured":"R\u00fchaak, J., et al.: Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans. Med. Imag. 36(8), 1746\u20131757 (2017)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"19_CR23","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AAA Setio","year":"2016","unstructured":"Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imag. 35(5), 1160\u20131169 (2016)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"19_CR24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.cviu.2008.06.006","volume":"112","author":"A Shekhovtsov","year":"2008","unstructured":"Shekhovtsov, A., Kovtun, I., Hlav\u00e1\u010d, V.: Efficient MRF deformation model for non-rigid image matching. Comput. Vis. Image Und. 112(1), 91\u201399 (2008)","journal-title":"Comput. Vis. Image Und."},{"issue":"5","key":"19_CR25","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imag. 35(5), 1285\u20131298 (2016)","journal-title":"IEEE Trans. Med. Imag."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of CVPR, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Veksler, O.: Stereo correspondence by dynamic programming on a tree. In: Proceedings of CVPR, vol. 2, pp. 384\u2013390. IEEE (2005)","DOI":"10.1109\/CVPR.2005.334"},{"key":"19_CR28","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.media.2018.11.010","volume":"52","author":"BD de Vos","year":"2019","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)","journal-title":"Med. Image Anal."},{"issue":"7","key":"19_CR29","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23(7), 903\u2013921 (2004)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"8","key":"19_CR30","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TBME.2016.2574816","volume":"63","author":"Z Xu","year":"2016","unstructured":"Xu, Z., et al.: Evaluation of 6 registration methods for the human abdomen on clinically acquired CT. IEEE Trans. Biomed. Eng. 63(8), 1563\u20131572 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of ICCV, pp. 1529\u20131537 (2015)","DOI":"10.1109\/ICCV.2015.179"},{"issue":"4","key":"19_CR32","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.media.2010.04.003","volume":"14","author":"D Zikic","year":"2010","unstructured":"Zikic, D., et al.: Linear intensity-based image registration by Markov random fields and discrete optimization. Med. Image Anal. 14(4), 550\u2013562 (2010)","journal-title":"Med. Image Anal."}],"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_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:04:02Z","timestamp":1759442642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59716-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597153","9783030597160"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59716-0_19","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)"}}]}}