{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:53:18Z","timestamp":1774273998606,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030875916","type":"print"},{"value":"9783030875923","type":"electronic"}],"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\/978-3-030-87592-3_11","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T02:17:10Z","timestamp":1632190630000},"page":"110-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image"],"prefix":"10.1007","author":[{"given":"Wanyue","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"11_CR1","first-page":"1","volume":"121","author":"W Li","year":"2020","unstructured":"Li, W.: Generating fundus fluorescence angiography images from structure fundus images using generative adversarial networks. Proc. Mach. Learn. Res. 121, 1\u201316 (2020)","journal-title":"Proc. Mach. Learn. Res."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"O\u2019Toole, L.: Fluorescein and ICG angiograms: still a gold standard. Acta Ophthalmol. Scand. 85 (2007)","DOI":"10.1111\/j.1600-0420.2007.01063_2988.x"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1097\/00006324-199908000-00017","volume":"76","author":"BJ Dolan","year":"1999","unstructured":"Dolan, B.J.: Fluorescein and ICG angiography textbook and atlas. Optom. Vis. Sci. 76, 520 (1999)","journal-title":"Optom. Vis. Sci."},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1038\/eye.2013.25","volume":"27","author":"DD Varma","year":"2013","unstructured":"Varma, D.D., Cugati, S., Lee, A.W., Chen, C.S.: A review of central retinal artery occlusion: clinical presentation and management. Eye 27, 688\u2013697 (2013)","journal-title":"Eye"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"2135","DOI":"10.1056\/NEJMcp1003934","volume":"363","author":"TY Wong","year":"2010","unstructured":"Wong, T.Y., Scott, I.U.: Retinal-vein occlusion. N. Engl. J. Med. 363, 2135\u20132144 (2010)","journal-title":"N. Engl. J. Med."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/S0161-6420(86)33697-2","volume":"93","author":"LA Yannuzzi","year":"1986","unstructured":"Yannuzzi, L.A., et al.: Fluorescein angiography complication survey. Ophthalmology 93, 611\u2013617 (1986)","journal-title":"Ophthalmology"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1111\/j.1600-0420.2006.00728.x","volume":"84","author":"F Musa","year":"2006","unstructured":"Musa, F., Muen, W.J., Hancock, R.: Adverse effects of fluorescein angiography in hypertensive and elderly patients. Acta Ophthalmol. Scand. 84, 740\u2013742 (2006)","journal-title":"Acta Ophthalmol. Scand."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Isola, P.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"11_CR9","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 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"11_CR10","unstructured":"Zhu, J.: Toward multimodal image-to-image translation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp.465\u2013476 (2017)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"11_CR12","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-662-56537-7_64","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2018","author":"F Schiffers","year":"2018","unstructured":"Schiffers, F., Yu, Z., Arguin, S., Maier, A., Ren, Q.: Synthetic fundus fluorescein angiography using deep neural networks. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds.) Bildverarbeitung f\u00fcr die Medizin 2018. Informatik aktuell, pp. 234\u2013238. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-662-56537-7_64"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Hervella, \u00c1.S.: Deep multimodal reconstruction of retinal images using paired or unpaired data. In: International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852082"},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-32778-1_4","volume-title":"Simulation and Synthesis in Medical Imaging","author":"K Li","year":"2019","unstructured":"Li, K., Yu, L., Wang, S., Heng, P.-A.: Unsupervised retina image synthesis via disentangled representation learning. In: Burgos, N., Gooya, A., Svoboda, D. (eds.) SASHIMI 2019. LNCS, vol. 11827, pp. 32\u201341. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32778-1_4"},{"key":"11_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-030-00928-1_37","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"\u00c1S Hervella","year":"2018","unstructured":"Hervella, \u00c1.S., Rouco, J., Novo, J., Ortega, M.: Retinal image understanding emerges from self-supervised multimodal reconstruction. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 321\u2013328. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_37"},{"key":"11_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-3-030-64559-5_10","volume-title":"Advances in Visual Computing","author":"SA Kamran","year":"2020","unstructured":"Kamran, S.A., Fariha Hossain, K., Tavakkoli, A., Zuckerbrod, S., Baker, S.A., Sanders, K.M.: Fundus2Angio: a conditional GAN architecture for generating fluorescein angiography images from retinal fundus photography. In: Bebis, G., et al. (eds.) ISVC 2020. LNCS, vol. 12510, pp. 125\u2013138. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64559-5_10"},{"key":"11_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Simulation and Synthesis in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87592-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T02:04:32Z","timestamp":1652148272000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87592-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875916","9783030875923"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87592-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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":"SASHIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Simulation and Synthesis in Medical Imaging","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":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sashimi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.sashimi-workshop.org\/","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":"OCS","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":"14","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":"78% - 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)"}}]}}