September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Endothelial cell recognition capability of paracentral and midperipheral corneal areas using automatic specular microscopy counting
Author Affiliations & Notes
  • Fábio Ursulino Reis Carvalho
    Ophthalmology, Santa Casa de São Paulo, São Paulo, São Paulo, Brazil
  • Richard Yudi Hida
    Ophthalmology, Santa Casa de São Paulo, São Paulo, São Paulo, Brazil
  • Ricardo Holzchuh
    Ophthalmology, Santa Casa de São Paulo, São Paulo, São Paulo, Brazil
  • Fernando Cesar Abib
    Universidade Federal do Paraná, Curitiba, Brazil
  • Footnotes
    Commercial Relationships   Fábio Carvalho, None; Richard Hida, None; Ricardo Holzchuh, None; Fernando Abib, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 1939. doi:
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      Fábio Ursulino Reis Carvalho, Richard Yudi Hida, Ricardo Holzchuh, Fernando Cesar Abib; Endothelial cell recognition capability of paracentral and midperipheral corneal areas using automatic specular microscopy counting. Invest. Ophthalmol. Vis. Sci. 2016;57(12):1939.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Despite the technological advances in capturing and analyzing corneal endothelium images by specular microscopy (SM), automatic counting of endothelial cells (EC) is subject to errors due to the acquired image quality and to the accuracy of the software. This study aims to compare the ability to recognize endothelial cells using automatic counting of a non-contact (NC) SM between paracentral areas (PCA) and midperipheral areas (MPA) from the endothelial mosaic.

Methods : The same examiner performed specular microscopy in the right eye of 36 consecutively examined patients with NC SM CEM-350 (NIDEK©). None of the patients had any eye disease or previous ocular surgery. Around the corneal center, 14 endothelial images were obtained in each examination: 8 images in PCA (Group I) and 6 in MPA (Group II). The SM software automatically counted the cells in each image. The EC recognition capability of this procedure was evaluated by comparing the analyzed image set by the SM with the EC image before analysis, considering: 1 – number (N) of non-counted EC groups and total number of EC in these groups; 2 – Erroneously counted EC: Split EC (one cell counted as two or more) and the average amount of EC created by this division, N of cell clusters (multiple cells counted as one) and the average amount of EC per cluster (Figure 1); 3 – Non-evaluated area of the image, calculated as 1-[counted cells x average cell area (µm2)/image size (µm2)]. We also compared the results of the SM parameters: counted cells (CC), endothelial cell density (ECD), average cell area (AVG), coefficient of variation (CV) and hexagonality percentage (HEX). Descriptive statistics and two-tailed Student’s t test was used for statistical analysis (p<0.05).

Results : Results from EC recognition capability analysis and SM parameters as well as the comparison between groups I and II are presented in table 1.

Conclusions : Errors in identifying the EC by automatic counting occurred both in PCA and in MPA of the endothelial mosaic, although they were more significant in MPA. Based on these results, manual cell counting or individualized assessment with correction of errors, to minimize distortions in the results of specular microscopy, are recommended.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

 

Three types of cell recognition errors. A=Non-counted cells; B=Cell cluster; C=Split cell; D=image C before automatic counting.

Three types of cell recognition errors. A=Non-counted cells; B=Cell cluster; C=Split cell; D=image C before automatic counting.

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