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Related Experiment Video

Updated: Dec 30, 2025

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

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Algorithm Variability in Quantification of Epithelial Defect Size in Microbial Keratitis Images.

Matthias F Kriegel^1,2, Jennifer Huang¹, Hamza A Ashfaq¹

¹Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, MI.

|January 25, 2020

Summary

This study found that image analysis algorithms reliably measure microbial keratitis (MK) epithelial defects from slit-lamp photos. Variability mainly stems from patient differences, not imaging or algorithm use.

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Published on: August 27, 2012

Area of Science:

Ophthalmology
Medical Imaging
Computational Biology

Background:

Microbial keratitis (MK) diagnosis and monitoring rely on accurate measurement of epithelial defects (ED).
Slit-lamp photography (SLP) is a common imaging modality for ocular surface diseases.
Semiautomated image-analysis algorithms offer potential for objective quantification of ED morphology.

Purpose of the Study:

To identify sources of measurement variability in quantifying microbial keratitis (MK) epithelial defects (ED) using slit-lamp photography (SLP) and a semiautomated image-analysis algorithm.
To assess the reliability and consistency of the image-analysis algorithm in measuring ED area.
To determine the contribution of imaging parameters and user application to overall measurement variability.

Main Methods:

Prospective enrollment of MK patients undergoing SLP with fluorescein staining.
Multiple imaging sessions under blue light at varying magnifications.
Annotation of epithelial defects and healthy cornea by a masked research assistant for algorithm input.
Estimation of variance components using a random effects model and calculation of intraclass correlation coefficients for reliability.

Main Results:

Analysis of 34 eyes (92 images, 274 seeds) after exclusions.
No significant differences in average ED area based on seeding or magnification (P > 0.5).
Minimal variability attributed to image (0.9%), magnification (0.2%), or seeding (0.1%).
Major variability (85.2%) originated from inter-patient differences in ED size.
High algorithm consistency demonstrated (ICC = 0.98).

Conclusions:

Semiautomated image-analysis algorithms demonstrate reliable performance for measuring ED area from SLP images.
The primary source of measurement variability is inherent differences in ED size between patients.
Imaging settings and user application of the algorithm contribute minimally to measurement variability, indicating robustness.