ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Machine learning for segmenting cells in corneal endothelium images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 25 citations in OpenAlex.
- Deep Learning Analysis of Widefield Cornea Endothelial Imaging in Fuchs Dystrophy.Ophthalmology science · 2026Article
- Progressive Insights into 3D Bioprinting for Corneal Tissue Restoration.Advanced healthcare materials · 2026Review
- Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.Bioengineering (Basel, Switzerland) · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- U-Net Convolutional Neural Network for Real-Time Prediction of the Number of Cultured Corneal Endothelial Cells for Cellular Therapy.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Unbiased image segmentation assessment toolkit for quantitative differentiation of state-of-the-art algorithms and pipelines.BMC bioinformatics · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.Scientific reports · 2022Article
- An automatic approach for cell detection and segmentation of corneal endothelium in specular microscope.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Overestimation of corneal endothelial cell density by automated method in glaucomatous eyes with impaired corneal endothelial cells.International ophthalmology · 2022Article
- Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy.Translational vision science & technology · 2021Article
- Advances in Imaging Technology of Anterior Segment of the Eye.Journal of ophthalmology · 2021Review
- Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.Translational vision science & technology · 2020Article
- Artificial intelligence in cornea and ocular surface diseases.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Images of the endothelial cell layer of the cornea can be used to evaluate corneal health. Quantitative biomarkers extracted from these images such as cell density, coefficient of variation of cell area, and cell hexagonality are commonly used to evaluate the status of the endothelium. Currently, fully-automated endothelial image analysis systems in use often give inaccurate results, while semi-automated methods, requiring trained image analysis readers to identify cells manually, are both challenging and time-consuming. We are investigating two deep learning methods to automatically segment cells in such images. We compare the performance of two deep neural networks, namely U-Net and SegNet. To train and test the classifiers, a dataset of 130 images was collected, with expert reader annotated cell borders in each image. We applied standard training and testing techniques to evaluate pixel-wise segmentation performance, and report corresponding metrics such as the Dice and Jaccard coefficients. Visual evaluation of results showed that most pixel-wise errors in the U-Net were rather non-consequential. Results from the U-Net approach are being applied to create endothelial cell segmentations and quantify important morphological measurements for evaluating cornea health.
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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.