{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:47:40Z","timestamp":1765295260561,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030869991"},{"type":"electronic","value":"9783030870003"}],"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\/978-3-030-87000-3_5","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T02:17:10Z","timestamp":1632190630000},"page":"42-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["FARGO: A Joint Framework for FAZ and\u00a0RV Segmentation from OCTA Images"],"prefix":"10.1007","author":[{"given":"Linkai","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pujin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"5_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":"2","key":"5_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s00417-017-3896-2","volume":"256","author":"M Ang","year":"2018","unstructured":"Ang, M., et al.: Optical coherence tomography angiography: a review of current and future clinical applications. Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 237\u2013245 (2018). https:\/\/doi.org\/10.1007\/s00417-017-3896-2","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"5_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"issue":"1","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"KK Cheng","year":"2021","unstructured":"Cheng, K.K., et al.: Macular vessel density, branching complexity and foveal avascular zone size in normal tension glaucoma. Sci. Rep. 11(1), 1\u20139 (2021)","journal-title":"Sci. Rep."},{"key":"5_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-32956-3_3","volume-title":"Ophthalmic Medical Image Analysis","author":"W Deng","year":"2019","unstructured":"Deng, W., Tamplin, M.R., Grumbach, I.M., Kardon, R.H., Garvin, M.K.: Region-based segmentation of capillary density in optical coherence tomography angiography. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) OMIA 2019. LNCS, vol. 11855, pp. 18\u201325. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32956-3_3"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"e0212364","DOI":"10.1371\/journal.pone.0212364","volume":"14","author":"M D\u00edaz","year":"2019","unstructured":"D\u00edaz, M., Novo, J., Cutr\u00edn, P., G\u00f3mez-Ulla, F., Penedo, M.G., Ortega, M.: Automatic segmentation of the foveal avascular zone in ophthalmological OCT-A images. PLoS ONE 14(2), e0212364 (2019)","journal-title":"PLoS ONE"},{"key":"5_CR7","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.compbiomed.2017.08.008","volume":"89","author":"N Eladawi","year":"2017","unstructured":"Eladawi, N., et al.: Automatic blood vessels segmentation based on different retinal maps from OCTA scans. Comput. Biol. Med. 89, 150\u2013161 (2017)","journal-title":"Comput. Biol. Med."},{"key":"5_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/978-3-319-46723-8_16","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"H Fu","year":"2016","unstructured":"Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via\u00a0deep learning and conditional random\u00a0field. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132\u2013139. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_16"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"6","key":"5_CR10","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1001\/archophthalmol.2011.2560","volume":"130","author":"R Klein","year":"2012","unstructured":"Klein, R., Myers, C.E., Lee, K.E., Gangnon, R., Klein, B.E.: Changes in retinal vessel diameter and incidence and progression of diabetic retinopathy. Arch. Ophthalmol. 130(6), 749\u2013755 (2012)","journal-title":"Arch. Ophthalmol."},{"issue":"13","key":"5_CR11","first-page":"948","volume":"50","author":"A Koskosas","year":"2009","unstructured":"Koskosas, A., Muldrew, K., Patton, W., Topouzis, F., Chakravarthy, U.: Foveal avascular zone (FAZ) area in aging and age related macular degeneration (AMD). Investig. Ophthalmol. Vis. Sci. 50(13), 948 (2009)","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Li, L., Verma, M., Nakashima, Y., Nagahara, H., Kawasaki, R.: IterNet: retinal image segmentation utilizing structural redundancy in vessel networks. In: The IEEE Winter Conference on Applications of Computer Vision (WACV), March 2020","DOI":"10.1109\/WACV45572.2020.9093621"},{"issue":"11","key":"5_CR13","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1109\/TMI.2020.2992244","volume":"39","author":"M Li","year":"2020","unstructured":"Li, M., et al.: Image projection network: 3d to 2d image segmentation in OCTA images. IEEE Trans. Med. Imaging 39(11), 3343\u20133354 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR14","unstructured":"Li, M., et al.: IPN-V2 and OCTA-500: methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261 (2020)"},{"key":"5_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/978-3-030-20873-8_40","volume-title":"Computer Vision \u2013 ACCV 2018","author":"H Liu","year":"2019","unstructured":"Liu, H., Wong, D.W.K., Fu, H., Xu, Y., Liu, J.: DeepAMD: detect early age-related macular degeneration by applying deep learning in a multiple instance learning framework. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 625\u2013640. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20873-8_40"},{"issue":"3","key":"5_CR16","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TMI.2020.3042802","volume":"40","author":"Y Ma","year":"2020","unstructured":"Ma, Y., et al.: ROSE: a retinal OCT-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imaging 40(3), 928\u2013939 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-32239-7_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Mou","year":"2019","unstructured":"Mou, L., et al.: CS-Net: channel and spatial attention network for curvilinear structure segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 721\u2013730. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_80"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"101874","DOI":"10.1016\/j.media.2020.101874","volume":"67","author":"L Mou","year":"2021","unstructured":"Mou, L., et al.: CS2-Net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)","journal-title":"Med. Image Anal."},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","volume":"90","author":"H Pratt","year":"2016","unstructured":"Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Proc. Comput. Sci. 90, 200\u2013205 (2016)","journal-title":"Proc. Comput. Sci."},{"key":"5_CR20","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":"5_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2017.11.003","volume":"64","author":"RF Spaide","year":"2018","unstructured":"Spaide, R.F., Fujimoto, J.G., Waheed, N.K., Sadda, S.R., Staurenghi, G.: Optical coherence tomography angiography. Prog. Retin. Eye Res. 64, 1\u201355 (2018)","journal-title":"Prog. Retin. Eye Res."},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Yip, V.C., et al.: Optical coherence tomography angiography of optic disc and macula vessel density in glaucoma and healthy eyes. J. Glaucoma 28(1), 80\u201387 (2019)","DOI":"10.1097\/IJG.0000000000001125"},{"key":"5_CR23","unstructured":"Zhang, H., et al.: RESNest: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/978-3-030-32239-7_88","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Zhang","year":"2019","unstructured":"Zhang, S., et al.: Attention guided network for retinal image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 797\u2013805. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_88"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"issue":"7","key":"5_CR26","doi-asserted-by":"publisher","first-page":"3653","DOI":"10.1167\/iovs.09-4935","volume":"51","author":"Y Zheng","year":"2010","unstructured":"Zheng, Y., Gandhi, J.S., Stangos, A.N., Campa, C., Broadbent, D.M., Harding, S.P.: Automated segmentation of foveal avascular zone in fundus fluorescein angiography. Invest. Ophthalmol. Vis. Sci. 51(7), 3653\u20133659 (2010)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"5_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS 2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"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\/978-3-030-87000-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T05:03:17Z","timestamp":1635742997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87000-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030869991","9783030870003"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87000-3_5","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":"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":"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":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omia8\/","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":"31","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":"20","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":"65% - 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)"}},{"value":"The workshop 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)"}}]}}