ArticleBioengineering (Basel, Switzerland)2024
Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Can YOLO Detect Retinal Pathologies? A Step Towards Automated OCT Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification.Scientific reports · 2025Article
- A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana.PloS one · 2025Article
- Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography.NPJ digital medicine · 2024Article
- Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.Bioengineering (Basel, Switzerland) · 2024Article
- AGSAM: Agent-Guided Segment Anything Model for Automatic Segmentation in Few-Shot Scenarios.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
This paper presents a novel U-Net model incorporating a hybrid attention mechanism for automating the segmentation of sub-retinal layers in Optical Coherence Tomography (OCT) images. OCT is an ophthalmology tool that provides detailed insights into retinal structures. Manual segmentation of these layers is time-consuming and subjective, calling for automated solutions. Our proposed model combines edge and spatial attention mechanisms with the U-Net architecture to improve segmentation accuracy. By leveraging attention mechanisms, the U-Net focuses selectively on image features. Extensive evaluations using datasets demonstrate that our model outperforms existing approaches, making it a valuable tool for medical professionals. The study also highlights the model's robustness through performance metrics such as an average Dice score of 94.99%, Adjusted Rand Index (ARI) of 97.00%, and Strength of Agreement (SOA) classifications like "Almost Perfect", "Excellent", and "Very Strong". This advanced predictive model shows promise in expediting processes and enhancing the precision of ocular imaging in real-world applications.
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Registered trials
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