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Opt. Express"],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:p>Accurate segmentation and quantification of retinal fluid in optical coherence tomography (OCT) images are important for assessing disease activity and treatment response in neovascular age-related macular degeneration (nvAMD). Yet, manual delineation of intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) is labor-intensive and subject to inter-grader variability. We developed a hybrid framework for robust retinal fluid segmentation in clinical-grade longitudinal OCT, combining anatomically constrained retinal-region extraction using Deep LOGISMOS with nnU-Net\u2013based 2-D and 3-D fluid segmentation. To improve adaptation to heterogeneous clinical data while reducing annotation burden, an iterative expert-guided labeling strategy was used, starting with 70 RETOUCH OCT scans and 100 scans from subjects with macular neovascularization at the University of Iowa and expanding to a final aggregated training set of 291 scans. The method was validated on a held-out test set of 50 OCT volumes from 50 nvAMD subjects, with a subset of 20 volumes independently annotated by a second expert for inter-observer analysis. It was further applied to a longitudinal dataset of 14,500 Heidelberg Spectralis macular OCT scans from 221 independent subjects. The ensemble model achieved Dice similarity coefficients (%) of 89.5\u00b111.7 for IRF, 88.0\u00b110.7 for SRF, and 82.0\u00b113.0 for PED against the primary expert annotations, with performance approaching expert-level agreement. In longitudinal inference, our developed framework supports robust large-scale quantification of fluid burden and spatial extent across heterogeneous scan protocols and extended follow-up. The artificial intelligence-based approach has the potential to enable scalable and clinically meaningful longitudinal analysis of retinal fluid in nvAMD.<\/jats:p>","DOI":"10.1364\/boe.599076","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T10:00:08Z","timestamp":1787306408000},"page":"4834","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Retinal fluid segmentation in real-world oct imaging of neovascular AMD using hybrid deep learning and graph-based optimization"],"prefix":"10.1364","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-6239","authenticated-orcid":true,"given":"Zhi","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernardo","family":"Bach","sequence":"additional","affiliation":[{"name":"University of Iowa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghai","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Wahle","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian C.","family":"Han","sequence":"additional","affiliation":[{"name":"University of Iowa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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