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Retinal Fluid Segmentation in Real-World OCT Imaging of Neovascular AMD Using Hybrid Deep Learning and Graph-Based Optimization
Journal article   Open access   Peer reviewed

Retinal Fluid Segmentation in Real-World OCT Imaging of Neovascular AMD Using Hybrid Deep Learning and Graph-Based Optimization

Zhi Chen, Bernardo Bach, Honghai Zhang, Andreas Wahle, Ian Han, H C Boldt, Stephen Russell, Milan Sonka and Elliott Sohn
Biomedical optics express, Vol.17(9), pp.4834-4853
09/01/2026
DOI: 10.1364/BOE.599076
PMCID: PMC13592241
PMID: 42770020
url
https://doi.org/10.1364/BOE.599076View
Published (Version of record) Open Access

Abstract

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–based 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±11.7 for IRF, 88.0±10.7 for SRF, and 82.0±13.0 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.

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