Assessment of the reliability of endothelial cell-density estimates in the presence of pseudoguttata

Doughty, Michael J.; Jonuscheit, Sven; Button, Norman F.

doi:10.1007/s00417-011-1812-8

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…On the contrary, the error may increase by estimating the local cell density of the image with guttae if the partial cells were included. 22 , 23 …”

Section: Discussionmentioning

confidence: 99%

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy

Huang

Maram

Tepelus

et al. 2018

Journal of Optometry

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PurposeTo determine the reliability of corneal endothelial cell density (ECD) obtained by automated specular microscopy versus that of validated manual methods and factors that predict such reliability.MethodsSharp central images from 94 control and 106 glaucomatous eyes were captured with Konan specular microscope NSP-9900. All images were analyzed by trained graders using Konan CellChek Software, employing the fully- and semi-automated methods as well as Center Method. Images with low cell count (input cells number <100) and/or guttata were compared with the Center and Flex-Center Methods. ECDs were compared and absolute error was used to assess variation. The effect on ECD of age, cell count, cell size, and cell size variation was evaluated.ResultsNo significant difference was observed between the Center and Flex-Center Methods in corneas with guttata (p = 0.48) or low ECD (p = 0.11). No difference (p = 0.32) was observed in ECD of normal controls <40 yrs old between the fully-automated method and manual Center Method. However, in older controls and glaucomatous eyes, ECD was overestimated by the fully-automated method (p = 0.034) and semi-automated method (p = 0.025) as compared to manual method.ConclusionOur findings show that automated analysis significantly overestimates ECD in the eyes with high polymegathism and/or large cell size, compared to the manual method. Therefore, we discourage reliance upon the fully-automated method alone to perform specular microscopy analysis, particularly if an accurate ECD value is imperative.

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“…On the contrary, the error may increase by estimating the local cell density of the image with guttae if the partial cells were included. 22 , 23 …”

Section: Discussionmentioning

confidence: 99%

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy

Huang

Maram

Tepelus

et al. 2018

Journal of Optometry

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“…The parameters pleomorphism and polymegethism are rarely used in routine practice because these parameters cannot be reliably determined in images with guttae or descemet’s folds 21 . CEC density, by contrast, can almost always be robustly extracted from CEC images of sufficient quality, even in the presence of guttae 22 .…”

Section: Methodsmentioning

confidence: 99%

Automated segmentation of the corneal endothelium in a large set of ‘real-world’ specular microscopy images using the U-Net architecture

Daniel

Atzrodt

Bucher

et al. 2019

Sci Rep

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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.

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“…2,7,13,16 The most reliable method for ECD estimation is generally considered to be planimetry, even when used to assess abnormal endothelia. 17 The technique is, however, time-consuming and cannot be implemented in routine clinical practice. Planimetry is based on identifying individual cells (for example, by drawing in the cell borders on an image), measuring the areas of these cells, and then an ECD is calculated from the average cell area value.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a variable frame (polygonal) method to estimate corneal endothelial cell counts after corneal transplantation

Jonuscheit

Doughty

Ramaesh

2012

Eye

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Assessment of the reliability of endothelial cell-density estimates in the presence of pseudoguttata

Abstract: Providing adequate numbers of complete cells are measured and in the absence of obvious polymegathism, ECD estimates can be made to within around ±3% in the presence of typical but significant pseudoguttata.

Cited by 5 publications

References 26 publications

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy

Automated segmentation of the corneal endothelium in a large set of ‘real-world’ specular microscopy images using the U-Net architecture

Assessment of a variable frame (polygonal) method to estimate corneal endothelial cell counts after corneal transplantation

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