ArticleJournal of digital imaging2021
RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review.Eye (London, England) · 2023Pooled it
- Retinal fluid segmentation in real-world OCT imaging of neovascular AMD using hybrid deep learning and graph-based optimization.Biomedical optics express · 2026Article
- Review
- RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.Journal of imaging informatics in medicine · 2025Article
- Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation.Biomedical optics express · 2025Article
- Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.Bioengineering (Basel, Switzerland) · 2024Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
- High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.Biomedical optics express · 2024Article
- Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images.Medical & biological engineering & computing · 2023Article
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography.Sensors (Basel, Switzerland) · 2023Review
- A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images.Scientific reports · 2023Article
- Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning.Journal of digital imaging · 2022Article
- Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course.Diagnostics (Basel, Switzerland) · 2022Article
- Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD.
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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.