{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:55:33Z","timestamp":1784040933447,"version":"3.55.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439988","type":"print"},{"value":"9783031439995","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43999-5_71","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"750-760","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Non-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable Image Registration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9562-1613","authenticated-orcid":false,"given":"Mingyuan","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-0200","authenticated-orcid":false,"given":"Lei","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0602-6319","authenticated-orcid":false,"given":"Michael","family":"Fulham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3381-214X","authenticated-orcid":false,"given":"Dagan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5960-1060","authenticated-orcid":false,"given":"Jinman","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"7","key":"71_CR1","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TMI.2013.2265603","volume":"32","author":"A Sotiras","year":"2013","unstructured":"Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153\u20131190 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"71_CR2","doi-asserted-by":"crossref","unstructured":"Meng, M., Liu, S.: High-quality panorama stitching based on asymmetric bidirectional optical flow. In: International Conference on Computational Intelligence and Applications (ICCIA), pp. 118\u2013122 (2020)","DOI":"10.1109\/ICCIA49625.2020.00030"},{"issue":"1","key":"71_CR3","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":"71_CR4","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. Meth. Programs Biomed. 98(3), 278\u2013284 (2010)","journal-title":"Comput. Meth. Programs Biomed."},{"key":"71_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s00138-020-01060-x","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31, 8 (2020)","journal-title":"Mach. Vis. Appl."},{"issue":"12","key":"71_CR6","doi-asserted-by":"publisher","first-page":"4895","DOI":"10.21037\/qims-21-175","volume":"11","author":"H Xiao","year":"2021","unstructured":"Xiao, H., et al.: A review of deep learning-based three-dimensional medical image registration methods. Quant. Imaging Med. Surg. 11(12), 4895\u20134916 (2021)","journal-title":"Quant. Imaging Med. Surg."},{"issue":"8","key":"71_CR7","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., et al.: 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":"71_CR8","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.media.2019.07.006","volume":"57","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., et al.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226\u2013236 (2019)","journal-title":"Med. Image Anal."},{"key":"71_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119444","volume":"259","author":"M Meng","year":"2022","unstructured":"Meng, M., et al.: Enhancing medical image registration via appearance adjustment networks. Neuroimage 259, 119444 (2022)","journal-title":"Neuroimage"},{"key":"71_CR10","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., et al.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128\u2013143 (2019)","journal-title":"Med. Image Anal."},{"key":"71_CR11","series-title":"LNCS","first-page":"257","volume-title":"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)"},{"key":"71_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, S., et al.: Recursive cascaded networks for unsupervised medical image registration. In: IEEE International Conference on Computer Vision, pp. 10600\u201310610 (2019)","DOI":"10.1109\/ICCV.2019.01070"},{"key":"71_CR13","series-title":"LNCS","first-page":"211","volume-title":"MICCAI 2020","author":"TCW Mok","year":"2020","unstructured":"Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 211\u2013221. Springer, Cham (2020)"},{"key":"71_CR14","series-title":"LNCS","first-page":"3","volume-title":"MICCAI 2021","author":"Y Shu","year":"2021","unstructured":"Shu, Y., et al.: Medical image registration based on uncoupled learning and accumulative enhancement. In: deBruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 3\u201313. Springer, Cham (2021)"},{"issue":"10","key":"71_CR15","doi-asserted-by":"publisher","first-page":"5130","DOI":"10.1109\/JBHI.2022.3189696","volume":"26","author":"B Hu","year":"2022","unstructured":"Hu, B., Zhou, S., Xiong, Z., Wu, F.: Recursive decomposition network for deformable image registration. IEEE J. Biomed. Health Inform. 26(10), 5130\u20135141 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"71_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102379","volume":"78","author":"M Kang","year":"2022","unstructured":"Kang, M., et al.: Dual-stream pyramid registration network. Med. Image Anal. 78, 102379 (2022)","journal-title":"Med. Image Anal."},{"issue":"10","key":"71_CR17","doi-asserted-by":"publisher","first-page":"2788","DOI":"10.1109\/TMI.2022.3170879","volume":"41","author":"J Lv","year":"2022","unstructured":"Lv, J., et al.: Joint progressive and coarse-to-fine registration of brain MRI via deformation field integration and non-rigid feature fusion. IEEE Trans. Med. Imaging 41(10), 2788\u20132802 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"71_CR18","series-title":"LNCS","first-page":"88","volume-title":"MICCAI 2022","author":"M Meng","year":"2022","unstructured":"Meng, M., Bi, L., Feng, D., Kim, J.: Non-iterative coarse-to-fine registration based on single-pass deep cumulative learning. In: Wang, L., et al. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 88\u201397. Springer, Cham (2022)"},{"key":"71_CR19","doi-asserted-by":"crossref","unstructured":"Meng, M., Bi, L., Feng, D. Kim, J.: Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision. arXiv preprint arXiv:2211.07876 (2022)","DOI":"10.1007\/978-3-031-33842-7_24"},{"key":"71_CR20","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00d716 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"71_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102615","volume":"82","author":"J Chen","year":"2022","unstructured":"Chen, J., et al.: Transmorph: transformer for unsupervised medical image registration. Med. Image Anal. 82, 102615 (2022)","journal-title":"Med. Image Anal."},{"key":"71_CR22","series-title":"LNCS","first-page":"78","volume-title":"MICCAI 2022","author":"Y Zhu","year":"2022","unstructured":"Zhu, Y., Lu, S.: Swin-voxelmorph: a symmetric unsupervised learning model for deformable medical image registration using swin transformer. In: Wang, L., et al. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 78\u201387. Springer, Cham (2022)"},{"key":"71_CR23","series-title":"LNCS","first-page":"217","volume-title":"MICCAI 2022","author":"J Shi","year":"2022","unstructured":"Shi, J., et al.: Xmorpher: full transformer for deformable medical image registration via cross attention. In: Wang, L., et al. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 217\u2013226. Springer, Cham (2022)"},{"key":"71_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"71_CR25","series-title":"LNCS","first-page":"646","volume-title":"MLMI 2019","author":"D Kuang","year":"2019","unstructured":"Kuang, D., Schmah, T.: Faim\u2013a convnet method for unsupervised 3d medical image registration. In: Suk, H.I., et al. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 646\u2013654. Springer, Cham (2019)"},{"issue":"1","key":"71_CR26","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"},{"issue":"1","key":"71_CR27","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","volume":"1","author":"SG Mueller","year":"2005","unstructured":"Mueller, S.G., et al.: Ways toward an early diagnosis in Alzheimer\u2019s disease: the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 1(1), 55\u201366 (2005)","journal-title":"Alzheimers Dement."},{"issue":"6","key":"71_CR28","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"D Martino","year":"2014","unstructured":"Martino, D., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"key":"71_CR29","doi-asserted-by":"crossref","unstructured":"ADHD-200 consortium.: the ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012)","DOI":"10.3389\/fnsys.2012.00062"},{"key":"71_CR30","unstructured":"The Information eXtraction from Images (IXI) dataset. https:\/\/brain-development.org\/ixi-dataset\/. Accessed 31 Oct 2022"},{"key":"71_CR31","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3389\/fnins.2012.00171","volume":"6","author":"A Klein","year":"2012","unstructured":"Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012)","journal-title":"Front. Neurosci."},{"key":"71_CR32","doi-asserted-by":"crossref","unstructured":"Fischl, B.: FreeSurfer. Neuroimage 62(2), 774\u2013781 (2012)","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"issue":"3","key":"71_CR33","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064\u20131080 (2008)","journal-title":"Neuroimage"},{"key":"71_CR34","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fninf.2014.00013","volume":"8","author":"M McCormick","year":"2014","unstructured":"McCormick, M., et al.: ITK: enabling reproducible research and open science. Front. Neuroinform. 8, 13 (2014)","journal-title":"Front. Neuroinform."},{"issue":"2","key":"71_CR35","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","volume":"5","author":"M Jenkinson","year":"2001","unstructured":"Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143\u2013156 (2001)","journal-title":"Med. Image Anal."},{"issue":"5","key":"71_CR36","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/JBHI.2019.2951024","volume":"24","author":"S Zhao","year":"2019","unstructured":"Zhao, S., et al.: Unsupervised 3D end-to-end medical image registration with volume tweening network. IEEE J. Biomed. Health Inform. 24(5), 1394\u20131404 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"71_CR37","unstructured":"Baheti, B., et al.: The brain tumor sequence registration challenge: establishing correspondence between pre-operative and follow-up MRI scans of diffuse glioma patients. arXiv preprint arXiv:2112.06979 (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43999-5_71","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:23:45Z","timestamp":1712075025000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43999-5_71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439988","9783031439995"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43999-5_71","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}