{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:47:43Z","timestamp":1758401263403,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031469138"},{"type":"electronic","value":"9783031469145"}],"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\/978-3-031-46914-5_6","type":"book-chapter","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T07:02:43Z","timestamp":1698649363000},"page":"65-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised Learning of\u00a0Cortical Surface Registration Using Spherical Harmonics"],"prefix":"10.1007","author":[{"given":"Seungeun","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunghwa","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seunghwan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5868-9603","authenticated-orcid":false,"given":"Ilwoo","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Barbaroux, H., Feng, X., Yang, J., Laine, A.F., Angelini, E.D.: Encoding human cortex using spherical CNNs-a study on Alzheimer\u2019s disease classification. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1322\u20131325. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098353"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Bayrak, R.G., Lyu, I., Chang, C.: Learning subject-specific functional parcellations from cortical surface measures. In: International Workshop on PRedictive Intelligence In MEdicine, pp. 172\u2013180. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16919-9_16","DOI":"10.1007\/978-3-031-16919-9_16"},{"issue":"1","key":"6_CR3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289\u2013300 (1995)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117161","volume":"221","author":"J Cheng","year":"2020","unstructured":"Cheng, J., Dalca, A.V., Fischl, B., Z\u00f6llei, L., Initiative, A.D.N., et al.: Cortical surface registration using unsupervised learning. Neuroimage 221, 117161 (2020)","journal-title":"Neuroimage"},{"key":"6_CR5","unstructured":"Chung, M.K.: Heat kernel smoothing on unit sphere. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, pp. 992\u2013995. IEEE (2006)"},{"issue":"2","key":"6_CR6","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"issue":"4","key":"6_CR7","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":"1","key":"6_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1023\/B:JMIV.0000011326.88682.e5","volume":"20","author":"J Glaun\u00e8s","year":"2004","unstructured":"Glaun\u00e8s, J., Vaillant, M., Miller, M.I.: Landmark matching via large deformation diffeomorphisms on the sphere. J. Math. Imaging Vis. 20(1), 179\u2013200 (2004)","journal-title":"J. Math. Imaging Vis."},{"issue":"10","key":"6_CR9","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1109\/TMI.2022.3168670","volume":"41","author":"S Ha","year":"2022","unstructured":"Ha, S., Lyu, I.: SPHARM-Net: Spherical harmonics-based convolution for cortical parcellation. IEEE Trans. Med. Imaging 41(10), 2739\u20132751 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3389\/fnins.2012.00171","volume":"6","author":"A Klein","year":"2012","unstructured":"Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012)","journal-title":"Front. Neurosci."},{"key":"6_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.117758","volume":"229","author":"I Lyu","year":"2021","unstructured":"Lyu, I., et al.: Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training. Neuroimage 229, 117758 (2021)","journal-title":"Neuroimage"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.media.2019.06.013","volume":"57","author":"I Lyu","year":"2019","unstructured":"Lyu, I., Kang, H., Woodward, N.D., Styner, M.A., Landman, B.A.: Hierarchical spherical deformation for cortical surface registration. Med. Image Anal. 57, 72\u201388 (2019)","journal-title":"Med. Image Anal."},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3389\/fnins.2015.00210","volume":"9","author":"I Lyu","year":"2015","unstructured":"Lyu, I., et al.: Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies. Front. Neurosci. 9, 210 (2015)","journal-title":"Front. Neurosci."},{"key":"6_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/978-3-030-59728-3_7","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"GH Ngo","year":"2020","unstructured":"Ngo, G.H., Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: From connectomic to task-evoked fingerprints: individualized prediction of task contrasts from resting-state functional connectivity. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 62\u201371. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_7"},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-32248-9_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"P Parvathaneni","year":"2019","unstructured":"Parvathaneni, P., et al.: Cortical Surface Parcellation Using Spherical Convolutional Neural Networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 501\u2013509. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_56"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.neuroimage.2014.05.069","volume":"100","author":"EC Robinson","year":"2014","unstructured":"Robinson, E.C., et al.: MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414\u2013426 (2014)","journal-title":"Neuroimage"},{"key":"6_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-87199-4_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S Sedlar","year":"2021","unstructured":"Sedlar, S., Alimi, A., Papadopoulo, T., Deriche, R., Deslauriers-Gauthier, S.: A spherical convolutional neural network for white matter structure imaging via dMRI. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 529\u2013539. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_50"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fninf.2018.00042","volume":"12","author":"SB Seong","year":"2018","unstructured":"Seong, S.B., Pae, C., Park, H.J.: Geometric convolutional neural network for analyzing surface-based neuroimaging data. Front. Neuroinform. 12, 42 (2018)","journal-title":"Front. Neuroinform."},{"key":"6_CR20","unstructured":"Sinzinger, F.L., Moreno, R.: Reinforcement learning based tractography with so (3) equivariant agents. In: Geometric Deep Learning in Medical Image Analysis (2022)"},{"key":"6_CR21","doi-asserted-by":"publisher","unstructured":"Suliman, M.A., Williams, L.Z., Fawaz, A., Robinson, E.C.: A deep-discrete learning framework for spherical surface registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 119\u2013129. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_12","DOI":"10.1007\/978-3-031-16446-0_12"},{"issue":"3","key":"6_CR22","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.neuroimage.2005.06.058","volume":"28","author":"DC Van Essen","year":"2005","unstructured":"Van Essen, D.C.: A population-average, landmark-and surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28(3), 635\u2013662 (2005)","journal-title":"Neuroimage"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Willbrand, E.H., et al.: Uncovering a tripartite landmark in posterior cingulate cortex. Sci. Adv. 8(36), eabn9516 (2022)","DOI":"10.1126\/sciadv.abn9516"},{"issue":"3","key":"6_CR24","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1109\/TMI.2009.2030797","volume":"29","author":"BT Yeo","year":"2009","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)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Yu, C., et al.: Validation of group-wise registration for surface-based functional MRI analysis. In: Proceedings of SPIE-the International Society for Optical Engineering, vol. 11596. NIH Public Access (2021)","DOI":"10.1117\/12.2580771"},{"issue":"8","key":"6_CR26","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TMI.2021.3069645","volume":"40","author":"F Zhao","year":"2021","unstructured":"Zhao, F., et al.: S3Reg: superfast spherical surface registration based on deep learning. IEEE Trans. Med. Imaging 40(8), 1964\u20131976 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/978-3-030-20351-1_67","volume-title":"Information Processing in Medical Imaging","author":"F Zhao","year":"2019","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"},{"key":"6_CR28","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","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46914-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T01:03:29Z","timestamp":1730423009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46914-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031469138","9783031469145"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46914-5_6","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":"31 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2023","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":"27","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":"23","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":"85% - 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":"3","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)"}}]}}