ArticleBiomedical optics express2017
Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Democratizing Glaucoma Care: A Framework for AI-Driven Progression Prediction Across Diverse Healthcare Settings.Journal of ophthalmology · 2025Article
- Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.Ophthalmology science · 2023Article
- Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography.Translational vision science & technology · 2022Article
- Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning.Frontiers in neuroscience · 2022Article
- Artificial intelligence and deep learning in ophthalmology - present and future (Review).Experimental and therapeutic medicine · 2020Review
- Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.Translational vision science & technology · 2020Article
- Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina.Current ophthalmology reports · 2020Article
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
- Automatic choroidal segmentation in OCT images using supervised deep learning methods.Scientific reports · 2019Article
- Deriving external forces via convolutional neural networks for biomedical image segmentation.Biomedical optics express · 2019Article
- Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images.Biomedical optics express · 2019Article
- Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks.Biomedical optics express · 2019Article
- Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search.Biomedical optics express · 2018Article
- [Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2018Review
- Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers.Biomedical optics express · 2018Article
- Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2.Biomedical optics express · 2018Article
- Applications of Artificial Intelligence in Ophthalmology: General Overview.Journal of ophthalmology · 2018Review
Corrections and comments
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Authors and funding
9 authors.
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Abstract
Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.
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