{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:11:59Z","timestamp":1758280319650,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440120"},{"type":"electronic","value":"9783031440137"}],"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\/978-3-031-44013-7_5","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T23:02:39Z","timestamp":1694818959000},"page":"42-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Recurrent Self Fusion: Iterative Denoising for\u00a0Consistent Retinal OCT Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8679-9615","authenticated-orcid":false,"given":"Shuwen","family":"Wei","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-9903","authenticated-orcid":false,"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-0650","authenticated-orcid":false,"given":"Zhangxing","family":"Bian","sequence":"additional","affiliation":[]},{"given":"Yuli","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5923-9097","authenticated-orcid":false,"given":"Lianrui","family":"Zuo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7776-6472","authenticated-orcid":false,"given":"Peter A.","family":"Calabresi","sequence":"additional","affiliation":[]},{"given":"Shiv","family":"Saidha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6553-0876","authenticated-orcid":false,"given":"Jerry L.","family":"Prince","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-5085","authenticated-orcid":false,"given":"Aaron","family":"Carass","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-017-0352-9","volume":"16","author":"K Alsaih","year":"2017","unstructured":"Alsaih, K., Lemaitre, G., Rastgoo, M., Massich, J., Sidib\u00e9, D., Meriaudeau, F.: Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed. Eng. Online 16, 1\u201312 (2017)","journal-title":"Biomed. Eng. Online"},{"issue":"8","key":"5_CR2","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."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Bhargava, P., et al.: Applying an open-source segmentation algorithm to different OCT devices in multiple sclerosis patients and healthy controls: implications for clinical trials. Multiple Sclerosis Int. 2015 (2015)","DOI":"10.1155\/2015\/136295"},{"issue":"4","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1364\/BOE.6.001172","volume":"6","author":"SJ Chiu","year":"2015","unstructured":"Chiu, S.J., Allingham, M.J., Mettu, P.S., Cousins, S.W., Izatt, J.A., Farsiu, S.: Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express 6(4), 1172\u20131194 (2015)","journal-title":"Biomed. Opt. Express"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"He, Y., et al.: 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":"5_CR6","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-030-32239-7_14"},{"issue":"5035","key":"5_CR7","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1126\/science.1957169","volume":"254","author":"D Huang","year":"1991","unstructured":"Huang, D., et al.: Optical coherence tomography. Science 254(5035), 1178\u20131181 (1991)","journal-title":"Science"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Lang, A., et al.: Retinal layer segmentation of macular oct images using boundary classification. Biomed. Opt. Express 4(7), 1133\u20131152 (2013)","DOI":"10.1364\/BOE.4.001133"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Leite, M.T., et al.: Agreement among spectral-domain optical coherence tomography instruments for assessing retinal nerve fiber layer thickness. Am. J. of Ophthalmol. 151(1), 85\u201392 (2011)","DOI":"10.1016\/j.ajo.2010.06.041"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Liu, Y., Zuo, L., Han, S., Xue, Y., Prince, J.L., Carass, A.: Coordinate translator for learning deformable medical image registration. In: Multiscale Multimodal Medical Imaging: Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, 22 September 2022, Proceedings, MICCAI 2022. LNCS, vol. 13594, pp. 98\u2013109. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18814-5_10","DOI":"10.1007\/978-3-031-18814-5_10"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Oguz, I., Malone, J.D., Atay, Y., Tao, Y.K.: Self-fusion for OCT noise reduction. In: Medical Imaging 2020: Image Processing, vol. 11313, pp. 45\u201350. SPIE (2020)","DOI":"10.1117\/12.2549472"},{"issue":"5","key":"5_CR12","doi-asserted-by":"publisher","first-page":"E652","DOI":"10.1097\/OPX.0b013e318238c34e","volume":"89","author":"NB Patel","year":"2012","unstructured":"Patel, N.B., Wheat, J.L., Rodriguez, A., Tran, V., Harwerth, R.S.: Agreement between retinal nerve fiber layer measures from Spectralis and Cirrus spectral domain OCT. Optomet. Vis. Sci. 89(5), E652 (2012)","journal-title":"Optomet. Vis. Sci."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Reaungamornrat, S., Carass, A., He, Y., Saidha, S., Calabresi, P.A., Prince, J.L.: Inter-scanner variation independent descriptors for constrained diffeomorphic Demons registration of retinal OCT. In: Proceedings of SPIE Medical Imaging (SPIE-MI 2018), Houston, 10\u201315 Feb. 2018, vol. 10574, p. 105741B (2018)","DOI":"10.1117\/12.2293790"},{"issue":"2","key":"5_CR14","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1002\/acn3.674","volume":"6","author":"A Rothman","year":"2019","unstructured":"Rothman, A., et al.: Retinal measurements predict 10-year disability in multiple sclerosis. Annal. Clin. Transl. Neurol. 6(2), 222\u2013232 (2019)","journal-title":"Annal. Clin. Transl. Neurol."},{"issue":"2","key":"5_CR15","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1093\/brain\/awq346","volume":"134","author":"S Saidha","year":"2011","unstructured":"Saidha, S., et al.: Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography. Brain 134(2), 518\u2013533 (2011)","journal-title":"Brain"},{"issue":"12","key":"5_CR16","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1177\/1352458511418630","volume":"17","author":"S Saidha","year":"2011","unstructured":"Saidha, S., et al.: Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness. Multip. Scleros. J. 17(12), 1449\u20131463 (2011)","journal-title":"Multip. Scleros. J."},{"issue":"11","key":"5_CR17","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1016\/S1474-4422(12)70213-2","volume":"11","author":"S Saidha","year":"2012","unstructured":"Saidha, S., et al.: Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study. Lancet Neurol. 11(11), 963\u2013972 (2012)","journal-title":"Lancet Neurol."},{"key":"5_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105368","volume":"144","author":"S Sotoudeh-Paima","year":"2022","unstructured":"Sotoudeh-Paima, S., Jodeiri, A., Hajizadeh, F., Soltanian-Zadeh, H.: Multi-scale convolutional neural network for automated AMD classification using retinal OCT images. Comput. Biol. Med. 144, 105368 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"5_CR19","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/ana.22005","volume":"67","author":"LS Talman","year":"2010","unstructured":"Talman, L.S., et al.: Longitudinal study of vision and retinal nerve fiber layer thickness in multiple sclerosis. Annal. Neurol. 67(6), 749\u2013760 (2010)","journal-title":"Annal. Neurol."},{"issue":"3","key":"5_CR20","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","volume":"35","author":"H Wang","year":"2012","unstructured":"Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Patt. Anal. Mach. Intell. 35(3), 611\u2013623 (2012)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"P126","DOI":"10.1016\/j.jalz.2016.06.205","volume":"12","author":"PA Yushkevich","year":"2016","unstructured":"Yushkevich, P.A., Pluta, J., Wang, H., Wisse, L.E., Das, S., Wolk, D.: IC-P-174: fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 Tesla and 7 Tesla T2-weighted MRI. Alzheimer\u2019s Dementia 12, P126\u2013P127 (2016)","journal-title":"Alzheimer\u2019s Dementia"}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44013-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T22:09:32Z","timestamp":1703196572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44013-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440120","9783031440137"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44013-7_5","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":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","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":"12 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":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omiax\/","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 System","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":"16","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":"59% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}