ArticleBiomedical optics express2019
Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 38 citations in OpenAlex.
- Structural and Angiographic Assessment of the Nerve Fiber Layer in Age-Related Macular Degeneration and Diabetic Retinopathy.Investigative ophthalmology & visual science · 2026Article
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- The ocular mathematical virtual simulator: A validated multiscale model for hemodynamics and biomechanics in the human eye.International journal for numerical methods in biomedical engineering · 2024Article
- Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders.Bioengineering (Basel, Switzerland) · 2023Article
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Artificial intelligence in OCT angiography.Progress in retinal and eye research · 2021Review
- An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses.Translational vision science & technology · 2021Article
- An end-to-end network for segmenting the vasculature of three retinal capillary plexuses from OCT angiographic volumes.Biomedical optics express · 2021Article
- DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography.IEEE transactions on bio-medical engineering · 2021Article
- Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.Biomedical optics express · 2021Article
- Artifacts and artifact removal in optical coherence tomographic angiography.Quantitative imaging in medicine and surgery · 2021Review
- Plexus-specific retinal vascular anatomy and pathologies as seen by projection-resolved optical coherence tomographic angiography.Progress in retinal and eye research · 2021Review
- Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.Translational vision science & technology · 2020Article
- Semi-supervised deep learning based 3D analysis of the peripapillary region.Biomedical optics express · 2020Article
- Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.
Identifiers
What OpenQuestion holds
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.