{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:43:59Z","timestamp":1778255039206,"version":"3.51.4"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"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\/978-3-031-43993-3_36","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"369-379","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Disentangling Site Effects with\u00a0Cycle-Consistent Adversarial Autoencoder for\u00a0Multi-site Cortical Data Harmonization"],"prefix":"10.1007","author":[{"given":"Fenqiang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengwang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dajiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Gilmore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weili","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"543","DOI":"10.3389\/fnins.2017.00543","volume":"11","author":"D Bzdok","year":"2017","unstructured":"Bzdok, D.: Classical statistics and statistical learning in imaging neuroscience. Front. Neurosci. 11, 543 (2017)","journal-title":"Front. Neurosci."},{"key":"36_CR2","doi-asserted-by":"publisher","first-page":"102799","DOI":"10.1016\/j.media.2023.102799","volume":"88","author":"S Cackowski","year":"2023","unstructured":"Cackowski, S., Barbier, E.L., Dojat, M., Christen, T.: ImUnity: a generalizable VAE-GAN solution for multicenter MR image harmonization. Med. Image Anal. 88, 102799 (2023)","journal-title":"Med. Image Anal."},{"issue":"3","key":"36_CR3","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968\u2013980 (2006)","journal-title":"Neuroimage"},{"key":"36_CR4","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.mri.2019.05.041","volume":"64","author":"BE Dewey","year":"2019","unstructured":"Dewey, B.E., et al.: DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging 64, 160\u2013170 (2019)","journal-title":"Magn. Reson. Imaging"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Ding, Z., et al.: Guided variational autoencoder for disentanglement learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7920\u20137929 (2020)","DOI":"10.1109\/CVPR42600.2020.00794"},{"issue":"2","key":"36_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"},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","volume":"167","author":"JP Fortin","year":"2018","unstructured":"Fortin, J.P., et al.: Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104\u2013120 (2018)","journal-title":"Neuroimage"},{"issue":"2","key":"36_CR8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","volume":"6","author":"GB Frisoni","year":"2010","unstructured":"Frisoni, G.B., Fox, N.C., Jack, C.R., Jr., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67\u201377 (2010)","journal-title":"Nat. Rev. Neurol."},{"issue":"1","key":"36_CR9","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"36_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"119863","DOI":"10.1016\/j.neuroimage.2023.119863","volume":"268","author":"H Guan","year":"2023","unstructured":"Guan, H., Liu, M.: DomainATM: domain adaptation toolbox for medical data analysis. NeuroImage 268, 119863 (2023)","journal-title":"NeuroImage"},{"issue":"7641","key":"36_CR12","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/nature21369","volume":"542","author":"HC Hazlett","year":"2017","unstructured":"Hazlett, H.C., et al.: Early brain development in infants at high risk for autism spectrum disorder. Nature 542(7641), 348\u2013351 (2017)","journal-title":"Nature"},{"key":"36_CR13","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.neuroimage.2018.03.049","volume":"185","author":"BR Howell","year":"2019","unstructured":"Howell, B.R., et al.: The UNC\/UMN baby connectome project (BCP): an overview of the study design and protocol development. Neuroimage 185, 891\u2013905 (2019)","journal-title":"Neuroimage"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Hu, F., et al.: Image harmonization: a review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. NeuroImage 274, 120125 (2023)","DOI":"10.1016\/j.neuroimage.2023.120125"},{"issue":"4","key":"36_CR15","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","volume":"15","author":"H Ij","year":"2018","unstructured":"Ij, H.: Statistics versus machine learning. Nat. Meth. 15(4), 233 (2018)","journal-title":"Nat. Meth."},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","volume":"109","author":"S Kazeminia","year":"2020","unstructured":"Kazeminia, S., et al.: GANs for medical image analysis. Artif. Intell. Med. 109, 101938 (2020)","journal-title":"Artif. Intell. Med."},{"key":"36_CR17","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)"},{"issue":"4","key":"36_CR18","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1002\/mrm.28243","volume":"84","author":"D Moyer","year":"2020","unstructured":"Moyer, D., Ver Steeg, G., Tax, C.M., Thompson, P.M.: Scanner invariant representations for diffusion MRI harmonization. Magn. Reson. Med. 84(4), 2174\u20132189 (2020)","journal-title":"Magn. Reson. Med."},{"key":"36_CR19","doi-asserted-by":"publisher","first-page":"116450","DOI":"10.1016\/j.neuroimage.2019.116450","volume":"208","author":"R Pomponio","year":"2020","unstructured":"Pomponio, R.: Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020)","journal-title":"Neuroimage"},{"key":"36_CR20","doi-asserted-by":"publisher","first-page":"119800","DOI":"10.1016\/j.neuroimage.2022.119800","volume":"265","author":"A Solanes","year":"2023","unstructured":"Solanes, A., et al.: Removing the effects of the site in brain imaging machine-learning-measurement and extendable benchmark. Neuroimage 265, 119800 (2023)","journal-title":"Neuroimage"},{"key":"36_CR21","doi-asserted-by":"publisher","first-page":"118703","DOI":"10.1016\/j.neuroimage.2021.118703","volume":"245","author":"ME Torbati","year":"2021","unstructured":"Torbati, M.E., et al.: A multi-scanner neuroimaging data harmonization using ravel and combat. Neuroimage 245, 118703 (2021)","journal-title":"Neuroimage"},{"issue":"32","key":"36_CR22","doi-asserted-by":"publisher","first-page":"15855","DOI":"10.1073\/pnas.1821523116","volume":"116","author":"F Wang","year":"2019","unstructured":"Wang, F., et al.: Developmental topography of cortical thickness during infancy. Proc. Natl. Acad. Sci. 116(32), 15855\u201315860 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Wang, L., Wu, Z., Chen, L., Sun, Y., Lin, W., Li, G.: iBEAT V2. 0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat. Protoc. 18, 1488\u20131509 (2023)","DOI":"10.1038\/s41596-023-00806-x"},{"issue":"2","key":"36_CR24","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1093\/cercor\/bhab213","volume":"32","author":"K Xia","year":"2022","unstructured":"Xia, K.: Genetic influences on longitudinal trajectories of cortical thickness and surface area during the first 2 years of life. Cereb. Cortex 32(2), 367\u2013379 (2022)","journal-title":"Cereb. Cortex"},{"key":"36_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-031-16446-0_68","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2022","author":"Y Xia","year":"2022","unstructured":"Xia, Y., Shi, Y.: Personalized DMRI harmonization on cortical surface. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 717\u2013725. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_68"},{"issue":"4","key":"36_CR26","doi-asserted-by":"publisher","first-page":"e3000042","DOI":"10.1371\/journal.pbio.3000042","volume":"17","author":"A Yamashita","year":"2019","unstructured":"Yamashita, A., et al.: Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 17(4), e3000042 (2019)","journal-title":"PLoS Biol."},{"issue":"4","key":"36_CR27","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1109\/TMI.2021.3050072","volume":"40","author":"F Zhao","year":"2021","unstructured":"Zhao, F., et al.: Spherical deformable U-Net: application to cortical surface parcellation and development prediction. IEEE Trans. Med. Imaging 40(4), 1217\u20131228 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"36_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-030-32251-9_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"F Zhao","year":"2019","unstructured":"Zhao, F., et al.: Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 475\u2013483. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_52"},{"key":"36_CR29","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.: 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":"36_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"36_CR31","doi-asserted-by":"publisher","first-page":"118569","DOI":"10.1016\/j.neuroimage.2021.118569","volume":"243","author":"L Zuo","year":"2021","unstructured":"Zuo, L., et al.: Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 243, 118569 (2021)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:09:51Z","timestamp":1712074191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"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":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}