Chan Yun Kim1, Seungsoo Rho2, Naeun Lee1, Chang-Kyu Lee1, Youngje Sung2. 1. Institute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, Korea. 2. Department of Ophthalmology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-712, Korea.
Abstract
OBJECTIVE: To evaluate the accuracy of a new semi-automated method for counting axons in transmission electron microscopic (TEM) images. PROCEDURES: Optic nerve cross sections were obtained from both eyes of Sprague Dawley rats after unilateral induction of chronic ocular hypertension. TEM images (3000× magnification) of cross sections were evaluated by both semi-automated and manual counting methods. The semi-automated counting method was performed using ImageJ software after simple image optimization, and the resulting estimate of axon damage was compared with semiquantitative damage grading scale from light microscopic (LM) images. RESULTS: Axon counts obtained from the semi-automated method were strongly correlated with those obtained from the manual counting method (Pearson's correlation coefficient r = 0.996, P < 0.001) and from the full manual count from LM images (Spearman's ρ = 0.973, P < 0.001). The semi-automated method measured axonal damage with an error of 0.94 ± 3.16% (mean ± standard deviation), with worse axonal damage associated with greater error. Interobserver and intra-observer variability in axons counts were low (Spearman's ρ = 0.999, P < 0.005). The results of the semi-automated counting method were highly correlated with semiquantitative damage grading scale (Spearman's ρ = 0.965, P < 0.001). CONCLUSION: Results of our semi-automated method for counting axons in TEM images were strongly correlated with those of conventional counting methods and showed excellent reproducibility.
OBJECTIVE: To evaluate the accuracy of a new semi-automated method for counting axons in transmission electron microscopic (TEM) images. PROCEDURES: Optic nerve cross sections were obtained from both eyes of Sprague Dawley rats after unilateral induction of chronic ocular hypertension. TEM images (3000× magnification) of cross sections were evaluated by both semi-automated and manual counting methods. The semi-automated counting method was performed using ImageJ software after simple image optimization, and the resulting estimate of axon damage was compared with semiquantitative damage grading scale from light microscopic (LM) images. RESULTS: Axon counts obtained from the semi-automated method were strongly correlated with those obtained from the manual counting method (Pearson's correlation coefficient r = 0.996, P < 0.001) and from the full manual count from LM images (Spearman's ρ = 0.973, P < 0.001). The semi-automated method measured axonal damage with an error of 0.94 ± 3.16% (mean ± standard deviation), with worse axonal damage associated with greater error. Interobserver and intra-observer variability in axons counts were low (Spearman's ρ = 0.999, P < 0.005). The results of the semi-automated counting method were highly correlated with semiquantitative damage grading scale (Spearman's ρ = 0.965, P < 0.001). CONCLUSION: Results of our semi-automated method for counting axons in TEM images were strongly correlated with those of conventional counting methods and showed excellent reproducibility.
Authors: Dylan A McCreedy; Daniel J Margul; Stephanie K Seidlits; Jennifer T Antane; Ryan J Thomas; Gillian M Sissman; Ryan M Boehler; Dominique R Smith; Sam W Goldsmith; Todor V Kukushliev; Jonathan B Lamano; Bansi H Vedia; Ting He; Lonnie D Shea Journal: J Neurosci Methods Date: 2016-01-25 Impact factor: 2.390
Authors: Barbara A Mysona; Sharmila Segar; Cecilia Hernandez; Christian Kim; Jing Zhao; David Mysona; Kathryn E Bollinger Journal: Transl Vis Sci Technol Date: 2020-02-18 Impact factor: 3.283
Authors: Berend O Broeren; Liron S Duraku; Caroline A Hundepool; Erik T Walbeehm; J Michiel Zuidam; Carlijn R Hooijmans; Tim De Jong Journal: PLoS One Date: 2021-12-02 Impact factor: 3.240
Authors: Kasra Zarei; Todd E Scheetz; Mark Christopher; Kathy Miller; Adam Hedberg-Buenz; Anamika Tandon; Michael G Anderson; John H Fingert; Michael David Abràmoff Journal: Sci Rep Date: 2016-05-26 Impact factor: 4.379