ArticleBiomedical optics express2017
Automatic detection of the foveal center in optical coherence tomography.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- Fine Tuning RETFound with Clinically Guided Foveal ROI for Automated DRIL Classification in Diabetic Macular Edema OCT.Diagnostics (Basel, Switzerland) · 2026Article
- Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.Medical & biological engineering & computing · 2025Article
- The Human Fovea Is Relatively Horizontally Elongated in Infantile Nystagmus.Investigative ophthalmology & visual science · 2025Article
- Advanced Analysis Tools for Two Wavelength Autofluorescence Imaging of Macular Xanthophyll Carotenoids: ALSTAR2 Baseline.Translational vision science & technology · 2025Observational
- Ganglion Cell Complex Analysis: Correlations with Retinal Nerve Fiber Layer on Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2023Review
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Automatic Determination of the Center of Macular Hole Using Optical Coherence Tomography En Face Images.Journal of clinical medicine · 2022Article
- Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Frontiers in endocrinology · 2022Review
- Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning.International journal of ophthalmology · 2022Article
- [Artificial intelligence in management of macular edema : Opportunities and challenges].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2020Review
- MACUSTAR: Development and Clinical Validation of Functional, Structural, and Patient-Reported Endpoints in Intermediate Age-Related Macular Degeneration.Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde · 2019Review
- [Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2018Review
- [Screening and management of retinal diseases using digital medicine].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
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Authors and funding
8 authors.
Funding
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Abstract
We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 μm, with a mean (± SD) distance of 71 μm ± 107 μm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 μm ± 84 μm and 56 μm ± 80 μm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.
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