ArticleJournal of medical imaging (Bellingham, Wash.)2020
Quantitative and qualitative evaluation of deep learning automatic segmentations of corneal endothelial cell images of reduced image quality obtained following cornea transplant.
Article in Journal of medical imaging (Bellingham, Wash.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Automatic Determination of Endothelial Cell Density From Donor Cornea Endothelial Cell Images.Translational vision science & technology · 2024Article
- Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis.Ophthalmology science · 2023Article
- Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system.Eye and vision (London, England) · 2023Article
- Machine Learning Analysis of Postkeratoplasty Endothelial Cell Images for the Prediction of Future Graft Rejection.Translational vision science & technology · 2023Article
- Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps.Biomedical optics express · 2023Article
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- 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
- Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification.Computational intelligence and neuroscience · 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
- Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells.Journal of biomedical optics · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We are developing automated analysis of corneal-endothelial-cell-layer, specular microscopic images so as to determine quantitative biomarkers indicative of corneal health following corneal transplantation. Especially on these images of varying quality, commercial automated image analysis systems can give inaccurate results, and manual methods are very labor intensive. We have developed a method to automatically segment endothelial cells with a process that included image flattening, U-Net deep learning, and postprocessing to create individual cell segmentations. We used 130 corneal endothelial cell images following one type of corneal transplantation (Descemet stripping automated endothelial keratoplasty) with expert-reader annotated cell borders. We obtained very good pixelwise segmentation performance (e.g., Dice
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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.