ReviewSensors (Basel, Switzerland)2022
Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- Review
- Clinical application of automated optical coherence tomography angiography for retinal fluid segmentation: a study of real-world data.Quantitative imaging in medicine and surgery · 2026Article
- Real-world evaluation of an OCT-based AI decision-support system for neovascular AMD activity triage in teleophthalmology.Frontiers in ophthalmology · 2026Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification.Scientific reports · 2025Article
- AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.Frontiers in public health · 2025Review
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Multimodal deep transfer learning to predict retinal vein occlusion macular edema recurrence after anti-VEGF therapy.Heliyon · 2024Article
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
- Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms.Diagnostics (Basel, Switzerland) · 2023Article
- Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.Scientific reports · 2023Article
- Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans.The EPMA journal · 2022Article
- Biological Signal Processing and Analysis for Healthcare Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography.Sensors (Basel, Switzerland) · 2022Article
- Automated Segmentation of Subretinal Fluid from OCT: A Vision Transformer Approach with Cross-Validation.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent years, the advanced machine learning methods, such as deep learning paradigms, have attracted more and more attention from academia in the retinal fluid segmentation applications. The automatic retinal fluid segmentation based on deep learning can improve the semantic segmentation accuracy and efficiency of macular change analysis, which has potential clinical implications for ophthalmic pathology detection. This article summarizes several different deep learning paradigms reported in the up-to-date literature for the retinal fluid segmentation in OCT images. The deep learning architectures include the backbone of convolutional neural network (CNN), fully convolutional network (FCN), U-shape network (U-Net), and the other hybrid computational methods. The article also provides a survey on the prevailing OCT image datasets used in recent retinal segmentation investigations. The future perspectives and some potential retinal segmentation directions are discussed in the concluding context.
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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.