ArticleTranslational vision science & technology2020
Pathological-Corneas Layer Segmentation and Thickness Measurement in OCT Images.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Automated quantification of central corneal epithelial thickness for the diagnosis of limbal stem cell deficiency.PloS one · 2026Article
- Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.Translational vision science & technology · 2025Article
- Inpainting Saturation Artifact in Anterior Segment Optical Coherence Tomography.Sensors (Basel, Switzerland) · 2023Article
- Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.Biomedical optics express · 2023Article
- Artificial Intelligence Segmentation Algorithm-Based Optical Coherence Tomography Image in Evaluation of Binocular Retinopathy.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Purpose: The purpose of this study was to propose a new algorithm for the segmentation and thickness measurement of pathological corneas with irregular layers using a two-stage graph search and ray tracing. Methods: In the first stage, a graph, with only gradient edge-cost, is used to segment the air-epithelium and endothelium-aqueous boundaries. In the second stage, a graph, with gradient, directional, and multiplier edge-cost, is used to correct segmentation. The optical coherence tomography (OCT) image is flattened using the air-epithelium boundary and a graph search is used to segment the epithelium-Bowman's and Bowman's-stroma boundaries. Then, the OCT image is flattened using the endothelium-aqueous boundary and a graph search is used to segment the Descemet's membrane. Ray tracing is used to correct the inter-boundary distances, then the thickness is measured using the shortest distance. The proposed algorithm was trained and evaluated using 190 OCT images manually segmented by trained operators. Results: The mean and standard deviation of the unsigned errors of the algorithm-operator and inter-operator were 0.89 ± 1.03 and 0.77 ± 0.68 pixels in segmentation and 3.62 ± 3.98 and 2.95 ± 2.52 µm in thickness measurement. Conclusions: Our proposed algorithm can produce accurate segmentation and thickness measurements compared with the manual operators. Translational Relevance: Our algorithm could be potentially useful in the clinical practice.
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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.