ArticleScientific reports2022
DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 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
- 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
- Deep Learning Analysis of Widefield Cornea Endothelial Imaging in Fuchs Dystrophy.Ophthalmology science · 2026Article
- A novel single-cell level evaluation method for corneal endothelial cell function.NPJ digital medicine · 2025Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-assisted widefield endothelial imaging in Descemet membrane endothelial keratoplasty.Frontiers in medicine · 2025Article
- 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
- Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.Bioengineering (Basel, Switzerland) · 2024Review
- U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation.Frontiers in neuroscience · 2024Article
- Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- 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
- Artificial intelligence in cornea and ocular surface diseases.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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7 authors.
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Abstract
Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To estimate the corneal parameters (endothelial cell density [ECD], coefficient of variation [CV], and hexagonality [HEX]), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 [cells/mm[Formula: see text]] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX. Compared with Topcon's built-in software, our error was 3-6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.
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