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Curr Eye Res. 2014 Sep;39(9):894-901. doi: 10.3109/02713683.2014.887120. Epub 2014 Mar 3.

Imaging and quantification of endothelial cell loss in eye bank prepared DMEK grafts using trainable segmentation software.

Author information

1
Casey Eye Institute, Oregon Health and Science University , Portland, OR , USA and.

Abstract

PURPOSE:

To improve accuracy and efficiency in quantifying the endothelial cell loss (ECL) in eye bank preparation of corneal endothelial grafts.

METHODS:

Eight cadaveric corneas were subjected to Descemet Membrane Endothelial Keratoplasty (DMEK) preparation. The endothelial surfaces were stained with a viability stain, calcein AM dye (CAM) and then captured by a digital camera. The ECL rates were quantified in these images by three separate readers using trainable segmentation, a plug-in feature from the imaging software, Fiji. Images were also analyzed by Adobe Photoshop for comparison. Mean times required to process the images were measured between the two modalities.

RESULTS:

The mean ECL (with standard deviation) as analyzed by Fiji was 22.5% (6.5%) and Adobe was 18.7% (7.0%; p = 0.04). The mean time required to process the images through the two different imaging methods was 19.9 min (7.5) for Fiji and 23.4 min (12.9) for Adobe (p = 0.17).

CONCLUSIONS:

Establishing an accurate, efficient and reproducible means of quantifying ECL in graft preparation and surgical techniques can provide insight to the safety, long-term potential of the graft tissues as well as provide a quality control measure for eye banks and surgeons. Trainable segmentation in Fiji software using CAM is a novel approach to measuring ECL that captured a statistically significantly higher percentage of ECL comparable to Adobe and was more accurate in standardized testing. Interestingly, ECL as determined using both methods in eye bank-prepared DMEK grafts exceeded 18% on average.

KEYWORDS:

Corneal transplant surgery; Descemet Membrane Endothelial Keratoplasty; endothelial cell loss; eye bank; trainable segmentation

PMID:
24588207
DOI:
10.3109/02713683.2014.887120
[Indexed for MEDLINE]

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