{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T09:17:23Z","timestamp":1773307043080,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164453","type":"print"},{"value":"9783031164460","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16446-0_2","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:02:47Z","timestamp":1663318967000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Double-Uncertainty Guided Spatial and\u00a0Temporal Consistency Regularization Weighting for\u00a0Learning-Based Abdominal Registration"],"prefix":"10.1007","author":[{"given":"Zhe","family":"Xu","sequence":"first","affiliation":[]},{"given":"Jie","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Donghuan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jiangpeng","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Sarah","family":"Frisken","sequence":"additional","affiliation":[]},{"given":"Jayender","family":"Jagadeesan","sequence":"additional","affiliation":[]},{"suffix":"III","given":"William M.","family":"Wells","sequence":"additional","affiliation":[]},{"given":"Xiu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Raymond Kai-yu","family":"Tong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"1","key":"2_CR1","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."},{"key":"2_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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252\u20139260 (2018)","DOI":"10.1109\/CVPR.2018.00964"},{"key":"2_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-030-32245-8_44","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"R Bhalodia","year":"2019","unstructured":"Bhalodia, R., Elhabian, S.Y., Kavan, L., Whitaker, R.T.: A cooperative autoencoder for population-based regularization of CNN image registration. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 391\u2013400. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_44"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/S1361-8415(97)85012-8","volume":"1","author":"F Bookstein","year":"1997","unstructured":"Bookstein, F.: Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med. Image Anal. 1, 225\u2013243 (1997)","journal-title":"Med. Image Anal."},{"key":"2_CR5","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":"2_CR6","unstructured":"Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. In: International Conference on Learning Representations (2016)"},{"key":"2_CR7","unstructured":"Ha, D., Dai, A., Le, Q.V.: Hypernetworks. arXiv preprint arXiv:1609.09106 (2016)"},{"issue":"7","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.media.2012.05.008","volume":"16","author":"MP Heinrich","year":"2012","unstructured":"Heinrich, M.P., et al.: MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423\u20131435 (2012)","journal-title":"Med. Image Anal."},{"issue":"7","key":"2_CR9","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. Imaging 32(7), 1239\u20131248 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR10","unstructured":"Hering, A., et al.: Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. arXiv preprint arXiv:2112.04489 (2021)"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-78191-0_1","volume-title":"Information Processing in Medical Imaging","author":"A Hoopes","year":"2021","unstructured":"Hoopes, A., Hoffmann, M., Fischl, B., Guttag, J., Dalca, A.V.: HyperMorph: amortized hyperparameter learning for image registration. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 3\u201317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_1"},{"key":"2_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1007\/978-3-030-00928-1_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Hu","year":"2018","unstructured":"Hu, Y., et al.: Adversarial deformation regularization for training image registration neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 774\u2013782. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_87"},{"key":"2_CR13","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017\u20132025 (2015)"},{"key":"2_CR14","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? arXiv preprint arXiv:1703.04977 (2017)"},{"key":"2_CR15","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., et al.: 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":"2_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-87202-1_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"TCW Mok","year":"2021","unstructured":"Mok, T.C.W., Chung, A.C.S.: Conditional deformable image registration with convolutional neural network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 35\u201345. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_4"},{"key":"2_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-030-59716-0_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C Qin","year":"2020","unstructured":"Qin, C., Wang, S., Chen, C., Qiu, H., Bai, W., Rueckert, D.: Biomechanics-informed neural networks for myocardial motion tracking in MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 296\u2013306. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_29"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Qu, Y., Mo, S., Niu, J.: DAT: training deep networks robust to label-noise by matching the feature distributions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6821\u20136829 (2021)","DOI":"10.1109\/CVPR46437.2021.00675"},{"key":"2_CR19","unstructured":"Smith, L., Gal, Y.: Understanding measures of uncertainty for adversarial example detection. arXiv preprint arXiv:1803.08533 (2018)"},{"key":"2_CR20","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195\u20131204 (2017)"},{"key":"2_CR21","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."},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-87196-3_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency\u00a0training. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 297\u2013306. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_28"},{"issue":"6","key":"2_CR23","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1007\/s11548-021-02359-4","volume":"16","author":"Z Xu","year":"2021","unstructured":"Xu, Z., Luo, J., Yan, J., Li, X., Jayender, J.: F3RNet: full-resolution residual registration network for deformable image registration. Int. J. Comput. Assist. Radiol. Surg. 16(6), 923\u2013932 (2021)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"2_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/978-3-030-59716-0_22","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Z Xu","year":"2020","unstructured":"Xu, Z., et al.: Adversarial uni- and multi-modal stream networks for multimodal image registration. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 222\u2013232. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_22"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yan, J., Luo, J., Li, X., Jagadeesan, J.: Unsupervised multimodal image registration with adaptative gradient Guidance. In: ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1225\u20131229. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414320"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yan, J., Luo, J., Wells, W., Li, X., Jagadeesan, J.: Unimodal cyclic regularization for training multimodal image registration networks. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1660\u20131664. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433926"},{"key":"2_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Yu","year":"2019","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16446-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:03:18Z","timestamp":1721372598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16446-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164453","9783031164460"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16446-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}