ArticleTranslational vision science & technology2020
Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Association Between Visual Acuity and Residual Retinal Fluid Following Intravitreal Anti-Vascular Endothelial Growth Factor Treatment for Neovascular Age-Related Macular Degeneration: A Systematic Review and Meta-analysis.JAMA ophthalmology · 2022Pooled it
- Automated Volumetric Segmentation of Microaneurysms on OCT Using Artificial Intelligence.ArXiv · 2026Article
- Retinal fluid segmentation in real-world OCT imaging of neovascular AMD using hybrid deep learning and graph-based optimization.Biomedical optics express · 2026Article
- Drusen volume and reticular pseudodrusen volume from optical coherence tomography with deep learning as risk factors for progression to late age-related macular degeneration in eyes with reticular pseudodrusen and contralateral macular neovascularisation.The British journal of ophthalmology · 2026Article
- Automated Segmentation and Characterization of Retinal Hyperreflective Foci in Age-Related Macular Degeneration.Translational vision science & technology · 2026Article
- AI-aided segmentation of four types of drusen in volumetric OCT.Biomedical optics express · 2025Article
- Nonperfused Retinal Capillaries-A New Method Developed on OCT and OCTA.Investigative ophthalmology & visual science · 2025Article
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm.Eye (London, England) · 2024Article
- Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.Bioengineering (Basel, Switzerland) · 2024Article
- Optical coherence tomography to identify upper airway obstruction sites in an apneic patient.Biophotonics discovery · 2024Article
- Novel Method to Measure Volumes of Retinal Specific Entities.Journal of clinical medicine · 2024Article
- Alterations of outer retinal reflectivity in diabetic patients without clinically detectable retinopathy.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map.IEEE transactions on bio-medical engineering · 2024Article
- Optical coherence tomography angiography in diabetic retinopathy.Progress in retinal and eye research · 2023Review
- Single-shot OCT and OCT angiography for slab-specific detection of diabetic retinopathy.Biomedical optics express · 2023Article
- Perfused and Nonperfused Microaneurysms Identified and Characterized by Structural and Angiographic OCT.ArXiv · 2023Article
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
- Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans.Diagnostics (Basel, Switzerland) · 2023Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT) volumes. Methods: The 3- × 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc., Fremont, CA, USA) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and six healthy controls, age 61.3 ± 10.1 (mean ± SD), 33% female, and all DR cases were diagnosed as severe NPDR or PDR). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net. The effect of including OCTA data for retinal fluid segmentation was investigated in this study. Volumetric retinal fluid can be constructed using the output of ReF-Net. Area-under-receiver-operating-characteristic-curve, intersection-over-union (IoU), and F1-score were calculated to evaluate the performance of ReF-Net. Results: ReF-Net shows high accuracy (F1 = 0.864 ± 0.084) in retinal fluid segmentation. The performance can be further improved (F1 = 0.892 ± 0.038) by including information from both OCTA and structural OCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetric retinal fluid can provide more comprehensive information than the two-dimensional (2D) area, whether cross-sectional or en face projections. Conclusions: A deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation. Volumetric representations of retinal fluid are superior to 2D projections. Translational Relevance: Using a deep learning method to segment retinal fluid volumetrically has the potential to improve the diagnostic accuracy of diabetic macular edema by OCT systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.