{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:41:39Z","timestamp":1742964099268,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164453"},{"type":"electronic","value":"9783031164460"}],"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_23","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:02:47Z","timestamp":1663318967000},"page":"237-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Collaborative Quantization Embeddings for\u00a0Intra-subject Prostate MR Image Registration"],"prefix":"10.1007","author":[{"given":"Ziyi","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianye","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Giganti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasilis","family":"Stavrinides","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Moore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirabela","family":"Rusu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey","family":"Sonn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Torr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dean","family":"Barratt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yipeng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"8","key":"23_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"},{"key":"23_CR2","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)"},{"issue":"6","key":"23_CR3","first-page":"5","volume":"370","author":"N Bloch","year":"2015","unstructured":"Bloch, N., et al.: NCI-ISBI 2013 challenge: automated segmentation of prostate structures. Cancer Imaging Arch. 370(6), 5 (2015)","journal-title":"Cancer Imaging Arch."},{"key":"23_CR4","unstructured":"Chen, K., Lee, C.G.: Incremental few-shot learning via vector quantization in deep embedded space. In: ICLR (2021)"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Chen, X., Meng, Y., Zhao, Y., Williams, R., Vallabhaneni, S.R., Zheng, Y.: Learning unsupervised parameter-specific affine transformation for medical images registration. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 24\u201334. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_3","DOI":"10.1007\/978-3-030-87202-1_3"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, D.H., Park, S.H., Kim, J., Lee, J.G., Ye, J.C.: CycleMorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71 (2021)","DOI":"10.1016\/j.media.2021.102036"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Kim, C.K., Park, B.K., Lee, H.M., Kim, S.S., Kim, E.: MRI techniques for prediction of local tumor progression after high-intensity focused ultrasonic ablation of prostate cancer. Am. J. Roentgenol. 190(5), 1180\u20131186 (2008)","DOI":"10.2214\/AJR.07.2924"},{"key":"23_CR8","doi-asserted-by":"publisher","unstructured":"Liu, F., et al.: SAME: deformable image registration based on self-supervised anatomical embeddings. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 87\u201397. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_9","DOI":"10.1007\/978-3-030-87202-1_9"},{"key":"23_CR9","unstructured":"Liu, L., Aviles-Rivero, A.I., Sch\u00f6nlieb, C.B.: Contrastive registration for unsupervised medical image segmentation. arXiv preprint arXiv:2011.08894 (2020)"},{"key":"23_CR10","unstructured":"Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. Journal of Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Modat, M., et al.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98(3), 278\u2013284 (2010)","DOI":"10.1016\/j.cmpb.2009.09.002"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with laplacian pyramid networks. In: Martel, A.L., Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 211\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_21","DOI":"10.1007\/978-3-030-59716-0_21"},{"key":"23_CR13","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: ICML, pp. 2498\u20132507. PMLR (2017)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Moore, C.M., et al.: Reporting magnetic resonance imaging in men on active surveillance for prostate cancer: the precise recommendations-a report of a european school of oncology task force. Eur. Urol. 71(4), 648\u2013655 (2017)","DOI":"10.1016\/j.eururo.2016.06.011"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Peng, J., Liu, D., Xu, S., Li, H.: Generating diverse structure for image inpainting with hierarchical VQ-VAE. In: CVPR. pp. 10775\u201310784 (2021)","DOI":"10.1109\/CVPR46437.2021.01063"},{"key":"23_CR16","unstructured":"Razavi, A., Van den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"23_CR17","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Schoots, I.G., Petrides, N., Giganti, F., Bokhorst, L.P., Rannikko, A., Klotz, L., Villers, A., Hugosson, J., Moore, C.M.: Magnetic resonance imaging in active surveillance of prostate cancer: a systematic review. Eur. Urol. 67(4), 627\u2013636 (2015)","DOI":"10.1016\/j.eururo.2014.10.050"},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Song, X., et al.: Cross-modal attention for mri and ultrasound volume registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 66\u201375. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_7","DOI":"10.1007\/978-3-030-87202-1_7"},{"key":"23_CR20","unstructured":"Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: Conference on Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, M.: DeepFlash: an efficient network for learning-based medical image registration. In: CVPR, pp. 4444\u20134452 (2020)","DOI":"10.1109\/CVPR42600.2020.00450"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Xu, J., Chen, E.Z., Chen, X., Chen, T., Sun, S.: Multi-scale neural odes for 3d medical image registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 213\u2013223. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_21","DOI":"10.1007\/978-3-030-87202-1_21"},{"key":"23_CR23","doi-asserted-by":"publisher","unstructured":"Xu, Zhenlin, Niethammer, Marc: DeepAtlas: joint semi-supervised learning of image registration and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 420\u2013429. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_47","DOI":"10.1007\/978-3-030-32245-8_47"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Yang, Q., Fu, Y., Giganti, F., Ghavami, N., Chen, Q., Noble, J.A., Vercauteren, T., Barratt, D., Hu, Y.: Longitudinal image registration with temporal-order and subject-specificity discrimination. In: MICCAI. pp. 243\u2013252. Springer (2020)","DOI":"10.1007\/978-3-030-59716-0_24"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Ye, M., Kanski, M., Yang, D., Chang, Q., Yan, Z., Huang, Q., Axel, L., Metaxas, D.: Deeptag: An unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images. In: CVPR. pp. 7261\u20137271 (June 2021)","DOI":"10.1109\/CVPR46437.2021.00718"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Zeng, Q., et al.: Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy. Phys. Med. Biol. 65(13) (2020)","DOI":"10.1088\/1361-6560\/ab8cd6"},{"key":"23_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, M., et al.: Frequency diffeomorphisms for efficient image registration. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 559\u2013570. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_44","DOI":"10.1007\/978-3-319-59050-9_44"},{"key":"23_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, Yungeng, Pei, Yuru, Zha, Hongbin: Learning Dual transformer network for\u00a0diffeomorphic registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 129\u2013138. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_13","DOI":"10.1007\/978-3-030-87202-1_13"}],"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_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:06:09Z","timestamp":1721372769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16446-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164453","9783031164460"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16446-0_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}