{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:01:16Z","timestamp":1775667676347,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031188138","type":"print"},{"value":"9783031188145","type":"electronic"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18814-5_10","type":"book-chapter","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T07:06:39Z","timestamp":1665644799000},"page":"98-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Coordinate Translator for\u00a0Learning Deformable Medical Image Registration"],"prefix":"10.1007","author":[{"given":"Yihao","family":"Liu","sequence":"first","affiliation":[]},{"given":"Lianrui","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Jerry L.","family":"Prince","sequence":"additional","affiliation":[]},{"given":"Aaron","family":"Carass","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"10_CR1","unstructured":"IXI Brain Development Dataset. https:\/\/brain-development.org\/ixi-dataset\/"},{"issue":"1","key":"10_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"issue":"365","key":"10_CR3","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight J. 2(365), 1\u201335 (2009)","journal-title":"Insight J."},{"issue":"8","key":"10_CR4","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":"4","key":"10_CR5","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TMI.2007.893283","volume":"26","author":"PL Bazin","year":"2007","unstructured":"Bazin, P.L., Pham, D.L.: Topology-preserving tissue classification of magnetic resonance brain images. IEEE Trans. Med. Imaging 26(4), 487\u2013496 (2007)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-319-66182-7_35","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"X Cao","year":"2017","unstructured":"Cao, X., et al.: Deformable image registration based on similarity-steered CNN regression. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 300\u2013308. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_35"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: transformer for unsupervised medical image registration. arXiv preprint arXiv:2111.10480 (2021)","DOI":"10.1016\/j.media.2022.102615"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chen, J., He, Y., Frey, E.C., Li, Y., Du, Y.: ViT-V-Net: vision transformer for unsupervised volumetric medical image registration. arXiv preprint arXiv:2104.06468 (2021)","DOI":"10.1016\/j.media.2022.102615"},{"issue":"9","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.cviu.2013.02.009","volume":"117","author":"CR Chou","year":"2013","unstructured":"Chou, C.R., Frederick, B., Mageras, G., Chang, S., Pizer, S.: 2D\/3D image registration using regression learning. Comput. Vis. Image Underst. 117(9), 1095\u20131106 (2013)","journal-title":"Comput. Vis. Image Underst."},{"key":"10_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/978-3-030-00928-1_82","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AV Dalca","year":"2018","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 729\u2013738. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_82"},{"issue":"2","key":"10_CR11","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1006\/cviu.1997.0605","volume":"66","author":"C Davatzikos","year":"1997","unstructured":"Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comput. Vis. Image Underst. 66(2), 207\u2013222 (1997)","journal-title":"Comput. Vis. Image Underst."},{"key":"10_CR12","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":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-319-67558-9_24","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"BD de Vos","year":"2017","unstructured":"de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., I\u0161gum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 204\u2013212. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_24"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.media.2019.03.006","volume":"54","author":"J Fan","year":"2019","unstructured":"Fan, J., Cao, X., Yap, P.T., Shen, D.: BIRNet: brain image registration using dual-supervised fully convolutional networks. Med. Image Anal. 54, 193\u2013206 (2019)","journal-title":"Med. Image Anal."},{"key":"10_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-540-40899-4_3","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2000","author":"M Ferrant","year":"2000","unstructured":"Ferrant, M., Warfield, S.K., Nabavi, A., Jolesz, F.A., Kikinis, R.: Registration of 3D intraoperative MR images of the brain using a finite element biomechanical model. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 19\u201328. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/978-3-540-40899-4_3"},{"issue":"2","key":"10_CR17","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":"10_CR18","doi-asserted-by":"publisher","first-page":"S102","DOI":"10.1016\/S1053-8119(09)70884-5","volume":"47","author":"V Fonov","year":"2009","unstructured":"Fonov, V., Evans, A., McKinstry, R., Almli, C., Collins, D.: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47, S102 (2009)","journal-title":"Neuroimage"},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-46726-9_3","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016","author":"B Guti\u00e9rrez-Becker","year":"2016","unstructured":"Guti\u00e9rrez-Becker, B., Mateus, D., Peter, L., Navab, N.: Learning optimization updates for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 19\u201327. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_3"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Han, R., et al.: Deformable MR-CT image registration using an unsupervised end-to-end synthesis and registration network for endoscopic neurosurgery. In: Medical Imaging 2021, vol. 11598, p. 1159819. International Society for Optics and Photonics (2021)","DOI":"10.1117\/12.2581567"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Han, R., et al.: Deformable MR-CT image registration using an unsupervised end-to-end synthesis and registration network for endoscopic neurosurgery. In: Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598, p. 1159819. International Society for Optics and Photonics (2021)","DOI":"10.1117\/12.2581567"},{"key":"10_CR22","unstructured":"Hering, A., et al.: Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. arXiv preprint arXiv:2112.04489 (2021)"},{"key":"10_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-78191-0_1","volume-title":"Information Processing in Medical Imaging","author":"A Hoopes","year":"2021","unstructured":"Hoopes, A., Hoffmann, M., Fischl, B., Guttag, J., Dalca, A.V.: HyperMorph: amortized hyperparameter learning for image registration. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 3\u201317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_1"},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neuroimage.2019.03.041","volume":"194","author":"Y Huo","year":"2019","unstructured":"Huo, Y., et al.: 3D whole brain segmentation using spatially localized atlas network tiles. Neuroimage 194, 105\u2013119 (2019)","journal-title":"Neuroimage"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Ilg, E., et al.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462\u20132470 (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"10_CR26","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2009","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196\u2013205 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., et al.: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv (2019)","DOI":"10.1101\/2019.12.13.19014902"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Lv, J., et al.: Joint progressive and coarse-to-fine registration of brain MRI via deformation field integration and non-rigid feature fusion. IEEE Trans. Med. Imaging (2022)","DOI":"10.1109\/TMI.2022.3170879"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Reinhold, J.C., et al.: Evaluating the impact of intensity normalization on MR image synthesis. In: Medical Imaging 2019: Image Processing, vol. 10949, p. 109493H. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2513089"},{"key":"10_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","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"},{"issue":"8","key":"10_CR32","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/42.796284","volume":"18","author":"D Rueckert","year":"1999","unstructured":"Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712\u2013721 (1999)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-97281-3_25","volume-title":"Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis","author":"H Siebert","year":"2022","unstructured":"Siebert, H., Hansen, L., Heinrich, M.P.: Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds.) MICCAI 2021. LNCS, vol. 13166, pp. 174\u2013179. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-97281-3_25"},{"issue":"3","key":"10_CR34","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S1361-8415(98)80022-4","volume":"2","author":"JP Thirion","year":"1998","unstructured":"Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell\u2019s demons. Med. Image Anal. 2(3), 243\u2013260 (1998)","journal-title":"Med. Image Anal."},{"issue":"6","key":"10_CR35","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR36","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"1","key":"10_CR37","doi-asserted-by":"publisher","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","volume":"45","author":"T Vercauteren","year":"2009","unstructured":"Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1), S61\u2013S72 (2009)","journal-title":"Neuroimage"},{"key":"10_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/978-3-030-87193-2_24","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"MK Wyburd","year":"2021","unstructured":"Wyburd, M.K., Dinsdale, N.K., Namburete, A.I.L., Jenkinson, M.: TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 250\u2013260. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_24"},{"key":"10_CR39","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration\u2013a deep learning approach. Neuroimage 158, 378\u2013396 (2017)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Multiscale Multimodal Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18814-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T07:08:28Z","timestamp":1665644908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18814-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031188138","9783031188145"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18814-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Multiscale Multimodal Medical Imaging","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":"22 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":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmmi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmmi2022.github.io\/","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":"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":"2,5","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":"1,67","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)"}}]}}