{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:29Z","timestamp":1759358489191,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030610555"},{"type":"electronic","value":"9783030610562"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61056-2_6","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T14:02:56Z","timestamp":1601647376000},"page":"73-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1823-1419","authenticated-orcid":false,"given":"Jian-Qing","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8520-7857","authenticated-orcid":false,"given":"Ngee Han","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8432-2511","authenticated-orcid":false,"given":"Bart\u0142omiej W.","family":"Papie\u017c","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,3]]},"reference":[{"key":"6_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/978-3-642-23629-7_63","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2011","author":"M Baiker","year":"2011","unstructured":"Baiker, M., Staring, M., L\u00f6wik, C.W.G.M., Reiber, J.H.C., Lelieveldt, B.P.F.: Automated registration of whole-body follow-up MicroCT data of mice. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 516\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23629-7_63"},{"key":"6_CR2","unstructured":"Chee, E., Wu, Z.: Airnet: self-supervised affine registration for 3d medical images using neural networks. arXiv preprint arXiv:1810.02583 (2018)"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"101631","DOI":"10.1016\/j.media.2019.101631","volume":"60","author":"M Dunnhofer","year":"2020","unstructured":"Dunnhofer, M., et al.: Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Med. Image Anal. 60, 101631 (2020)","journal-title":"Med. Image Anal."},{"issue":"3","key":"6_CR4","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s11548-018-1875-7","volume":"14","author":"G Haskins","year":"2019","unstructured":"Haskins, G., et al.: Learning deep similarity metric for 3D MR-TRUS image registration. Int. J. Comput. Assist. Radiol. Surg. 14(3), 417\u2013425 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"6_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(1), 8 (2020)","journal-title":"Mach. Vis. Appl."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1070\u20131074. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363756"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1007\/978-3-030-32248-9_94","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"D Kwon","year":"2019","unstructured":"Kwon, D., et al.: Siamese U-net with healthy template for accurate segmentation of intracranial hemorrhage. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 848\u2013855. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_94"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Liao, R., et al.: An artificial agent for robust image registration. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11230"},{"key":"6_CR9","unstructured":"Lim, N.H., Fowkes, M.M.: Radiopaque compound containing diiodotyrosine, 5 Jun 2019, EU Patent EP3490614A1"},{"key":"6_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-66182-7_28","volume-title":"Medical Image Computing and Computer Assisted Intervention -MICCAI 2017","author":"K Ma","year":"2017","unstructured":"Ma, K., et al.: Multimodal image registration with deep context reinforcement learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_28"},{"issue":"4","key":"6_CR11","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.media.2010.07.002","volume":"15","author":"Y Ou","year":"2011","unstructured":"Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622\u2013639 (2011)","journal-title":"Med. Image Anal."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-030-32248-9_94","volume-title":"Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging","author":"BW Papie\u017c","year":"2016","unstructured":"Papie\u017c, B.W., Szmul, A., Grau, V., Brady, J.M., Schnabel, J.A.: Non-local graph-based regularization for deformable image registration. In: M\u00fcller, H., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 199\u2013207. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_94"},{"issue":"2","key":"6_CR13","first-page":"470","volume":"38","author":"SSM Salehi","year":"2018","unstructured":"Salehi, S.S.M., Khan, S., Erdogmus, D., Gholipour, A.: Real-time deep pose estimation with geodesic loss for image-to-template rigid registration. IEEE TMI 38(2), 470\u2013481 (2018)","journal-title":"IEEE TMI"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.media.2016.06.031","volume":"33","author":"JA Schnabel","year":"2016","unstructured":"Schnabel, J.A., Heinrich, M.P., Papie\u017c, B.W., Brady, J.M.: Advances and challenges in deformable image registration: from image fusion to complex motion modelling. Med. Image Anal. 33, 145\u2013148 (2016)","journal-title":"Med. Image Anal."},{"key":"6_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1007\/978-3-030-32248-9_94","volume-title":"Medical Image Computing and Computer Assisted Intervention -MICCAI 2019","author":"D Kwon","year":"2019","unstructured":"Kwon, D., et al.: Siamese U-net with healthy template for accurate segmentation of intracranial hemorrhage. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 848\u2013855. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_94"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Sloan, J.M., Goatman, K.A., Siebert, J.P.: Learning rigid image registration-utilizing convolutional neural networks for medical image registration. In: 11th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 89\u201399. SCITEPRESS-Science and Technology Publications (2018)","DOI":"10.5220\/0006543700890099"},{"key":"6_CR17","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":"6_CR18","unstructured":"Wang, C., Papanastasiou, G., Chartsias, A., Jacenkow, G., Tsaftaris, S.A., Zhang, H.: FIRE: unsupervised bi-directional inter-modality registration using deep networks. arXiv preprint arXiv:1907.05062 (2019)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5745\u20135753 (2019)","DOI":"10.1109\/CVPR.2019.00589"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61056-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:07:56Z","timestamp":1759356476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61056-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030610555","9783030610562"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61056-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","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":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","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":"12","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":"67% - 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":"2","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":"2","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":"No","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 took place 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)"}}]}}