ArticleScientific reports2019
Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Agentic Artificial Intelligence in Eye Banking: A Proposed Workflow.Eye banking and corneal transplantation · 2026Article
- Recent advances in corneal specular microscopy image analysis through artificial intelligence.PLOS digital health · 2026Review
- Artificial intelligence in corneal endothelial disease and endothelial keratoplasty: current evidence, clinical readiness, and future directions.Frontiers in ophthalmology · 2026Review
- A novel single-cell level evaluation method for corneal endothelial cell function.NPJ digital medicine · 2025Article
- Deep learning-assisted widefield endothelial imaging in Descemet membrane endothelial keratoplasty.Frontiers in medicine · 2025Article
- Medical image segmentation with UNet-based multi-scale context fusion.Scientific reports · 2024Article
- Automatic Determination of Endothelial Cell Density From Donor Cornea Endothelial Cell Images.Translational vision science & technology · 2024Article
- A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.The international journal of cardiovascular imaging · 2024Article
- Deep learning for detection of Fuchs endothelial dystrophy from widefield specular microscopy imaging: a pilot study.Eye and vision (London, England) · 2024Article
- 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
- Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- [Corneal endothelial cell photography: comparison of smartphones].Die Ophthalmologie · 2023Article
- Machine Learning Analysis of Postkeratoplasty Endothelial Cell Images for the Prediction of Future Graft Rejection.Translational vision science & technology · 2023Article
- Addressing bias in manual segmentation of spheroid sprouting assays with U-Net.Molecular vision · 2023Article
- Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps.Biomedical optics express · 2023Article
- 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
- Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy.Translational vision science & technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring the density of corneal endothelial cells (CEC) is essential in the management of corneal diseases. Its manual calculation is time consuming and prone to errors. U-Net, a neural network for biomedical image segmentation, has shown promising results in the automated segmentation of images of healthy corneas and good quality. The purpose of this study was to assess its performance in "real-world" CEC images (variable quality, different ophthalmologic diseases). The outcome measures were: precision and recall of the extraction of CEC, correctness of CEC density estimation, detection of ungradable images. A classical approach based on grayscale morphology and water shedding was pursued for comparison. There was good agreement between the automated image analysis and the manual annotation from the U-Net. R-square from Pearson's correlation was 0.96. Recall of CEC averaged 0.34 and precision 0.84. The U-Net correctly predicted the CEC density in a large set of images of healthy and diseased corneas, including images of poor quality. It robustly ignored image regions with poor visibility of CEC. The classical approach, however, did not provide acceptable results. R-square from Pearson's correlation with the ground truth was as low as 0.35.
Indexed as
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.