{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:40:40Z","timestamp":1742992840371,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872366"},{"type":"electronic","value":"9783030872373"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87237-3_11","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"108-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images"],"prefix":"10.1007","author":[{"given":"Hong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Dong","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Donghuan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yuexiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Liansheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","volume":"3","author":"MD Abr\u00e0moff","year":"2010","unstructured":"Abr\u00e0moff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169\u2013208 (2010)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"12","key":"11_CR2","doi-asserted-by":"publisher","first-page":"2712","DOI":"10.1364\/BOE.4.002712","volume":"4","author":"BJ Antony","year":"2013","unstructured":"Antony, B.J., Abr\u00e0moff, M.D., Harper, M.M., et al.: A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes. Biomed. Opt. Express 4(12), 2712\u20132728 (2013)","journal-title":"Biomed. Opt. Express"},{"issue":"8","key":"11_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. Imag. 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1167\/iovs.15-17281","volume":"57","author":"JC Bavinger","year":"2016","unstructured":"Bavinger, J.C., Dunbar, G.E., Stem, M.S., et al.: The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry. Invest. Ophthalmol. Vis. Sci. 57(1), 208\u2013217 (2016)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"4","key":"11_CR5","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1364\/BOE.5.001062","volume":"5","author":"A Carass","year":"2014","unstructured":"Carass, A., Lang, A., Hauser, M., Calabresi, P.A., Ying, H.S., Prince, J.L.: Multiple-object geometric deformable model for segmentation of macular OCT. Biomed. Opt. Express 5(4), 1062\u20131074 (2014)","journal-title":"Biomed. Opt. Express"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z.l., Wei, H., Shen, H.l., et\u00a0al.: Intraretinal layer segmentation and parameter measurement in optic nerve head region through energy function of spatial-gradient continuity constraint. J. Cent. South Univ. 25(8), 1938\u20131947 (2018)","DOI":"10.1007\/s11771-018-3884-7"},{"key":"11_CR7","volume-title":"Kee Wong","author":"J Cheng","year":"2016","unstructured":"Cheng, J., Lee, J.A., Xu, G., Quan, Y., Ong, E.P.: Kee Wong. Motion correction in optical coherence tomography for multi-modality retinal image registration, D.W. (2016)"},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.preteyeres.2007.07.005","volume":"27","author":"W Drexler","year":"2008","unstructured":"Drexler, W., Fujimoto, J.G.: State-of-the-art retinal optical coherence tomography. Prog. Retin. Eye Res. 27(1), 45\u201388 (2008)","journal-title":"Prog. Retin. Eye Res."},{"issue":"1","key":"11_CR9","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ophtha.2013.07.013","volume":"121","author":"S Farsiu","year":"2014","unstructured":"Farsiu, S., Chiu, S.J., O\u2019Connell, R.V., et al.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1), 162\u2013172 (2014)","journal-title":"Ophthalmology"},{"issue":"9","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1109\/TMI.2009.2016958","volume":"28","author":"MK Garvin","year":"2009","unstructured":"Garvin, M.K., Abramoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imag. 28(9), 1436\u20131447 (2009)","journal-title":"IEEE Trans. Med. Imag."},{"key":"11_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-32239-7_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y He","year":"2019","unstructured":"He, Y., et al.: Fully convolutional boundary regression for retina OCT segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 120\u2013128. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_14"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"He, Y., Carass, A., Liu, Y., et\u00a0al.: Structured layer surface segmentation for retina OCT using fully convolutional regression networks. Med. Image Anal. 68, 101856 (2021)","DOI":"10.1016\/j.media.2020.101856"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Huang, D., Swanson, E.A., Lin, C.P., et\u00a0al.: Optical coherence tomography. Science 254(5035), 1178\u20131181 (1991)","DOI":"10.1126\/science.1957169"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Kansal, V., Armstrong, J.J., Pintwala, R., Hutnik, C.: Optical coherence tomography for glaucoma diagnosis: an evidence based meta-analysis. PloS one 13(1), e0190621 (2018)","DOI":"10.1371\/journal.pone.0190621"},{"issue":"7","key":"11_CR15","doi-asserted-by":"publisher","first-page":"3378","DOI":"10.1167\/iovs.08-2728","volume":"50","author":"PA Keane","year":"2009","unstructured":"Keane, P.A., Liakopoulos, S., Jivrajka, R.V., et al.: Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 50(7), 3378\u20133385 (2009)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","volume":"6","author":"J Ker","year":"2017","unstructured":"Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375\u20139389 (2017)","journal-title":"IEEE Access"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.dadm.2016.07.004","volume":"4","author":"B Knoll","year":"2016","unstructured":"Knoll, B., Simonett, J., Volpe, N.J., et al.: Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: case-control study and meta-analysis. Alzheimer\u2019s Dementia Diagnosis Assessment Disease Monitoring 4, 85\u201393 (2016)","journal-title":"Alzheimer\u2019s Dementia Diagnosis Assessment Disease Monitoring"},{"issue":"7","key":"11_CR18","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1364\/BOE.4.001133","volume":"4","author":"A Lang","year":"2013","unstructured":"Lang, A., Carass, A., Hauser, M., et al.: Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express 4(7), 1133\u20131152 (2013)","journal-title":"Biomed. Opt. Express"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Li, H., Fan, Y.: Non-rigid image registration using fully convolutional networks with deep self-supervision. arXiv preprint arXiv:1709.00799 (2017)","DOI":"10.1109\/ISBI.2018.8363757"},{"issue":"6","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TMI.2017.2666045","volume":"36","author":"J Novosel","year":"2017","unstructured":"Novosel, J., Vermeer, K.A., De Jong, J.H., Wang, Z., Van Vliet, L.J.: Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas. IEEE Trans. Med. Imag. 36(6), 1276\u20131286 (2017)","journal-title":"IEEE Trans. Med. Imag."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jneumeth.2017.07.031","volume":"291","author":"EA Pnevmatikakis","year":"2017","unstructured":"Pnevmatikakis, E.A., Giovannucci, A.: NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83\u201394 (2017)","journal-title":"J. Neurosci. Methods"},{"key":"11_CR22","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"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Roy, A.G., et al.: ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627\u20133642 (2017)","DOI":"10.1364\/BOE.8.003627"},{"issue":"2","key":"11_CR24","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1093\/brain\/awq346","volume":"134","author":"S Saidha","year":"2011","unstructured":"Saidha, S., Syc, S.B., Ibrahim, M.A., et al.: Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography. Brain 134(2), 518\u2013533 (2011)","journal-title":"Brain"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.media.2019.02.004","volume":"54","author":"A Shah","year":"2019","unstructured":"Shah, A., Ab\u00e1moff, M.D., Wu, X.: Optimal surface segmentation with convex priors in irregularly sampled space. Med. Image Anal. 54, 63\u201375 (2019)","journal-title":"Med. Image Anal."},{"issue":"9","key":"11_CR26","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1364\/BOE.9.004509","volume":"9","author":"A Shah","year":"2018","unstructured":"Shah, A., Zhou, L., Abr\u00e1moff, M.D., Wu, X.: Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images. Biomed. Opt. Express 9(9), 4509\u20134526 (2018)","journal-title":"Biomed. Opt. Express"},{"issue":"12","key":"11_CR27","doi-asserted-by":"publisher","first-page":"4174","DOI":"10.1109\/TMI.2020.3014433","volume":"39","author":"S Wang","year":"2020","unstructured":"Wang, S., Cao, S., Chai, Z., et al.: Conquering data variations in resolution: a slice-aware multi-branch decoder network. IEEE Trans. Med. Imag. 39(12), 4174\u20134185 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"11_CR28","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1109\/TBME.2018.2875955","volume":"66","author":"D Wei","year":"2018","unstructured":"Wei, D., Weinstein, S., Hsieh, M.K., Pantalone, L., Kontos, D.: Three-dimensional whole breast segmentation in sagittal and axial breast MRI with dense depth field modeling and localized self-adaptation for chest-wall line detection. IEEE Trans. Biomed. Eng. 66(6), 1567\u20131579 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-642-04271-3_79","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009","author":"A Yazdanpanah","year":"2009","unstructured":"Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic, M.: Intra-retinal layer segmentation in optical coherence tomography using an active contour approach. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 649\u2013656. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04271-3_79"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, Y., Zhang, P., Chen, H., Xia, Y., Shen, C.: Light-weight hybrid convolutional network for liver tumor segmentation. In: IJCAI, pp. 4271\u20134277 (2019)","DOI":"10.24963\/ijcai.2019\/593"},{"key":"11_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/978-3-030-32251-9_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., et al.: Models genesis: generic autodidactic models for 3D medical image analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 384\u2013393. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_42"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87237-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:00:14Z","timestamp":1632380414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","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)"}}]}}