{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T21:00:49Z","timestamp":1758402049450,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"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_12","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:02:47Z","timestamp":1663318967000},"page":"119-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Deep-Discrete Learning Framework for\u00a0Spherical Surface Registration"],"prefix":"10.1007","author":[{"given":"Mohamed A.","family":"Suliman","sequence":"first","affiliation":[]},{"given":"Logan Z. J.","family":"Williams","sequence":"additional","affiliation":[]},{"given":"Abdulah","family":"Fawaz","sequence":"additional","affiliation":[]},{"given":"Emma C.","family":"Robinson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"1","key":"12_CR1","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1006\/nimg.1999.0516","volume":"11","author":"K Amunts","year":"2000","unstructured":"Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., Zilles, K.: Brodmann\u2019s areas 17 and 18 brought into stereotaxic space-where and how variable? Neuroimage 11(1), 66\u201384 (2000)","journal-title":"Neuroimage"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S.: Pointnetlk: robust & efficient point cloud registration using pointnet. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7163\u20137172 (2019)","DOI":"10.1109\/CVPR.2019.00733"},{"issue":"8","key":"12_CR3","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":"10","key":"12_CR4","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/TMI.2020.2986331","volume":"39","author":"J Borovec","year":"2020","unstructured":"Borovec, J., et al.: ANHIR: automatic non-rigid histological image registration challenge. IEEE Trans. Med. Imaging 39(10), 3042\u20133052 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"27","key":"12_CR5","doi-asserted-by":"publisher","first-page":"E6356","DOI":"10.1073\/pnas.1801582115","volume":"115","author":"TS Coalson","year":"2018","unstructured":"Coalson, T.S., Van Essen, D.C., Glasser, M.F.: The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl. Acad. Sci. 115(27), E6356\u2013E6365 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"12_CR6","unstructured":"Dalca, A., Rakic, M., Guttag, J., Sabuncu, M.: Learning conditional deformable templates with convolutional networks. In: 33rd Advances in neural Information Processing Systems, vol. 32 (2019)"},{"key":"12_CR7","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":"12_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/978-3-030-00928-1_83","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Fan","year":"2018","unstructured":"Fan, J., Cao, X., Xue, Z., Yap, P.-T., Shen, D.: Adversarial similarity network for evaluating image alignment in deep learning based registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 739\u2013746. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_83"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Fawaz, A., et al.: Benchmarking geometric deep learning for cortical segmentation and neurodevelopmental phenotype prediction. bioRxiv (2021)","DOI":"10.1101\/2021.12.01.470730"},{"key":"12_CR10","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"issue":"4","key":"12_CR11","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4","volume":"8","author":"B Fischl","year":"1999","unstructured":"Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272\u2013284 (1999)","journal-title":"Hum. Brain Mapp."},{"issue":"4","key":"12_CR12","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1002\/mp.14065","volume":"47","author":"Y Fu","year":"2020","unstructured":"Fu, Y., et al.: LungregNet: an unsupervised deformable image registration method for 4D-CT lung. Med. Phys. 47(4), 1763\u20131774 (2020)","journal-title":"Med. Phys."},{"issue":"7615","key":"12_CR13","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/nature18933","volume":"536","author":"ME Glasser","year":"2016","unstructured":"Glasser, M.E., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171\u2013178 (2016)","journal-title":"Nature"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","volume":"80","author":"ME Glasser","year":"2013","unstructured":"Glasser, M.E., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105\u2013124 (2013)","journal-title":"Neuroimage"},{"key":"12_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/978-3-030-32226-7_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"MP Heinrich","year":"2019","unstructured":"Heinrich, M.P.: Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 50\u201358. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_6"},{"key":"12_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-030-59716-0_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"MP Heinrich","year":"2020","unstructured":"Heinrich, M.P., Hansen, L.: Highly accurate and memory efficient unsupervised learning-based discrete CT registration using 2.5d displacement search. In: Martel, M.P., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 190\u2013200. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_19"},{"key":"12_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"12_CR18","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in neural Information Processing Systems, vol. 24 (2011)"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115\u20135124 (2017)","DOI":"10.1109\/CVPR.2017.576"},{"key":"12_CR20","first-page":"18433","volume":"33","author":"N Pielawski","year":"2020","unstructured":"Pielawski, N., et al.: CoMIR: contrastive multimodal image representation for registration. Adv. Neural. Inf. Process. Syst. 33, 18433\u201318444 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR21","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"12_CR22","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.neuroimage.2017.10.037","volume":"167","author":"EK Robinson","year":"2018","unstructured":"Robinson, E.K., et al.: Multimodal surface matching with higher-order smoothness constraints. Neuroimage 167, 453\u2013465 (2018)","journal-title":"Neuroimage"},{"key":"12_CR23","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.neuroimage.2014.05.069","volume":"100","author":"EK Robinson","year":"2014","unstructured":"Robinson, E.K., et al.: MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414\u2013426 (2014)","journal-title":"Neuroimage"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Shao, W., et al.: ProsRegNet: a deep learning framework for registration of MRI and histopathology images of the prostate. Med. Image Anal. 68 (2021)","DOI":"10.1016\/j.media.2020.101919"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Suliman, M.A., Williams, L.Z., Fawaz, A., Robinson, E.C.: A deep-discrete learning framework for spherical surface registration. arXiv preprint arXiv:2203.12999 (2022)","DOI":"10.1007\/978-3-031-16446-0_12"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Y., Solomon, J.M.: Deep closest point: learning representations for point cloud registration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3523\u20133532 (2019)","DOI":"10.1109\/ICCV.2019.00362"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Yeo, B.T., Sabuncu, M.R., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29(3), 650\u2013668 (2009)","DOI":"10.1109\/TMI.2009.2030797"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, F., et al.: S3reg: superfast spherical surface registration based on deep learning. IEEE Trans. Med. Imaging 40(8), 1964\u20131976 (2021)","DOI":"10.1109\/TMI.2021.3069645"},{"key":"12_CR29","doi-asserted-by":"publisher","unstructured":"Zhao, F., et al.: Spherical U-net on cortical surfaces: methods and applications. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 855\u2013866. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_67","DOI":"10.1007\/978-3-030-20351-1_67"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529\u20131537 (2015)","DOI":"10.1109\/ICCV.2015.179"},{"key":"12_CR31","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5745\u20135753 (2019)","DOI":"10.1109\/CVPR.2019.00589"}],"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_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:04:23Z","timestamp":1721372663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16446-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164453","9783031164460"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16446-0_12","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)"}}]}}