{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:17:56Z","timestamp":1753273076767,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031353017"},{"type":"electronic","value":"9783031353024"}],"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\/978-3-031-35302-4_39","type":"book-chapter","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T07:02:40Z","timestamp":1686812560000},"page":"375-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deformable Image Registration Using Vision Transformers for\u00a0Cardiac Motion Estimation from\u00a0Cine Cardiac MRI Images"],"prefix":"10.1007","author":[{"given":"Roshan Reddy","family":"Upendra","sequence":"first","affiliation":[]},{"given":"Richard","family":"Simon","sequence":"additional","affiliation":[]},{"given":"Suzanne M.","family":"Shontz","sequence":"additional","affiliation":[]},{"given":"Cristian A.","family":"Linte","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"issue":"8","key":"39_CR1","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"},{"issue":"11","key":"39_CR2","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR3","unstructured":"Chen, J., et al.: TransUnet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., He, Y., Frey, E.C., Li, Y., Du, Y.: ViT-V-net: vision transformer for unsupervised volumetric medical image registration. arXiv preprint arXiv:2104.06468 (2021)","DOI":"10.1016\/j.media.2022.102615"},{"key":"39_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"39_CR6","first-page":"1","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vision Appl. 31(1), 1\u201318 (2020)","journal-title":"Mach. Vision Appl."},{"key":"39_CR7","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"issue":"1","key":"39_CR8","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"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Marstal, K., Berendsen, F., Staring, M., Klein, S.: SimpleElastix: a user-friendly, multi-lingual library for medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 134\u2013142 (2016)","DOI":"10.1109\/CVPRW.2016.78"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"39_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-00129-2_7","volume-title":"Machine Learning for Medical Image Reconstruction","author":"C Qin","year":"2018","unstructured":"Qin, C., et al.: Joint motion estimation and segmentation from undersampled cardiac MR image. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 55\u201363. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00129-2_7"},{"key":"39_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-39074-7_20","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"H Qiu","year":"2020","unstructured":"Qiu, H., Qin, C., Le Folgoc, L., Hou, B., Schlemper, J., Rueckert, D.: Deep learning for cardiac motion estimation: supervised vs. unsupervised training. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 186\u2013194. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_20"},{"key":"39_CR13","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":"39_CR14","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/42.796284","volume":"18","author":"D Rueckert","year":"1999","unstructured":"Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712\u2013721 (1999)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Upendra, R.R., et al.: Motion extraction of the right ventricle from 4D cardiac cine MRI using a deep learning-based deformable registration framework. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3795\u20133799. IEEE (2021)","DOI":"10.1109\/EMBC46164.2021.9630586"},{"key":"39_CR16","unstructured":"Upendra, R.R., Wentz, B.J., Shontz, S.M., Linte, C.A.: A convolutional neural network-based deformable image registration method for cardiac motion estimation from cine cardiac MR images. In: 2020 Computing in Cardiology, pp. 1\u20134. IEEE (2020)"},{"key":"39_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-030-78710-3_25","volume-title":"Functional Imaging and Modeling of the Heart","author":"RR Upendra","year":"2021","unstructured":"Upendra, R.R., Wentz, B.J., Simon, R., Shontz, S.M., Linte, C.A.: CNN-based cardiac motion extraction to generate deformable geometric left ventricle myocardial models from cine MRI. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds.) FIMH 2021. LNCS, vol. 12738, pp. 253\u2013263. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78710-3_25"},{"key":"39_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"2","key":"39_CR19","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TMI.2011.2171706","volume":"31","author":"H Wang","year":"2011","unstructured":"Wang, H., Amini, A.A.: Cardiac motion and deformation recovery from MRI: a review. IEEE Trans. Med. Imaging 31(2), 487\u2013503 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-87193-2_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"W Wang","year":"2021","unstructured":"Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: multimodal brain tumor segmentation using transformer. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 109\u2013119. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_11"},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., You, C., Ahn, S., Zhuang, J., Staib, L., Duncan, J.: Learning correspondences of cardiac motion from images using biomechanics-informed modeling. arXiv preprint arXiv:2209.00726 (2022)","DOI":"10.1007\/978-3-031-23443-9_2"}],"container-title":["Lecture Notes in Computer Science","Functional Imaging and Modeling of the Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35302-4_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:09:11Z","timestamp":1691593751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35302-4_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031353017","9783031353024"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35302-4_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FIMH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Functional Imaging and Modeling of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"20 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fimh2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2023.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Eqiunocs","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"80","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":"72","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":"90% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}