ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2022
An automatic approach for cell detection and segmentation of corneal endothelium in specular microscope.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 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
10 citing papers in PubMed, 9 citations in OpenAlex.
- 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
- A novel single-cell level evaluation method for corneal endothelial cell function.NPJ digital medicine · 2025Article
- Changes in corneal endothelial cell loss in different regions in patients with cataracts and peripheral anterior synechiae after phacoemulsification combined with goniosynechialysis.Frontiers in medicine · 2025Article
- Corneal Endothelial Microscopy: Does a Manual Recognition of the Endothelial Cells Help the Morphometric Analysis Compared to a Fully Automatic Approach?Vision (Basel, Switzerland) · 2024Article
- Automatic Determination of Endothelial Cell Density From Donor Cornea Endothelial Cell Images.Translational vision science & technology · 2024Article
- Deep learning for detection of Fuchs endothelial dystrophy from widefield specular microscopy imaging: a pilot study.Eye and vision (London, England) · 2024Article
- A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation.Frontiers in oncology · 2023Article
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.Scientific reports · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSpecular microscopy is an indispensable tool for clinicians seeking to monitor the corneal endothelium. Automated methods of determining endothelial cell density (ECD) are limited in their ability to analyze images of poor quality. We describe and assess an image processing algorithm to analyze corneal endothelial images.
methodsA set of corneal endothelial images acquired with a Konan CellChek specular microscope was analyzed using three methods: flex-center, Konan Auto Tracer, and the proposed method. In this technique, the algorithm determines the region of interest, filters the image to differentiate cell boundaries from their interiors, and utilizes stochastic watershed segmentation to draw cell boundaries and assess ECD based on the masked region. We compared ECD measured by the algorithm with manual and automated results from the specular microscope.
resultsWe analyzed a total of 303 images manually, using the Auto Tracer, and with the proposed image processing method. Relative to manual analysis across all images, the mean error was 0.04% in the proposed method (p = 0.23 for difference) whereas Auto Tracer demonstrated a bias towards overestimation, with a mean error of 5.7% (p = 2.06× 10
conclusionWe demonstrate a computationally efficient algorithm to analyze corneal endothelial cell density that can be implemented on devices for clinical and research use.
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.