ArticleBiomedical optics express2017
Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- Effect of Optical Coherence Tomography Angiography Image Artifacts on Quantitation of Vessel Density and Perfusion.Ophthalmic surgery, lasers & imaging retina · 2026Article
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- Quantifying Explainability in OCT Segmentation of Macular Holes and Cysts: A SHAP-Based Coverage and Factor Contribution Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Quantitative and longitudinal monitoring of cancer cell invasion in a three-dimensional in vitro model of oral cancer using optical coherence tomography.Scientific reports · 2025Article
- Segmentation of 3D OCT Images of Human Skin Using Neural Networks with U-Net Architecture.Sovremennye tekhnologii v meditsine · 2025Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Validation of Inter-Reader Agreement/Consistency for Quantification of Ellipsoid Zone Integrity and Sub-RPE Compartmental Features Across Retinal Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Peripheral Choroidal Response to Localized Defocus Blur: Influence of Native Peripheral Aberrations.Investigative ophthalmology & visual science · 2024Article
- Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning.Applied sciences (Basel, Switzerland) · 2023Article
- Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.International journal of ophthalmology · 2023Article
- Article
- A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation.Scientific reports · 2022Article
- Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks.Scientific reports · 2022Article
- Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).Journal of digital imaging · 2022Article
- Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks.Biomedical optics express · 2022Article
- OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN.Sensors (Basel, Switzerland) · 2022Article
- Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator.Scientific reports · 2022Article
- A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022Review
- Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images.Radiology research and practice · 2022Review
6 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
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Abstract
We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.
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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.