title: Automated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice.
abstract: OBJECTIVE
  To describe an adaptation of an existing graph-theoretic method (initially developed for human optical coherence tomography [OCT] images) for the three-dimensional (3D) automated segmentation of 10 intraretinal surfaces in mice scans, and assess the accuracy of the method and the reproducibility of thickness measurements.
  
  
  METHODS
  Ten intraretinal surfaces were segmented in repeat spectral domain (SD)-OCT volumetric images acquired from normal (n= 8) and diabetic (n= 10) mice. The accuracy of the method was assessed by computing the border position errors of the automated segmentation with respect to manual tracings obtained from two experts. The reproducibility was statistically assessed for four retinal layers within eight predefined regions using the mean and SD of the differences in retinal thickness measured in the repeat scans, the coefficient of variation (CV) and the intraclass correlation coefficients (ICC; with 95% confidence intervals [CIs]).
  
  
  RESULTS
  The overall mean unsigned border position error for the 10 surfaces computed over 97 B-scans (10 scans, 10 normal mice) was 3.16 ± 0.91 μm. The overall mean differences in retinal thicknesses computed from the normal and diabetic mice were 1.86 ± 0.95 and 2.15 ± 0.86 μm, respectively. The CV of the retinal thicknesses for all the measured layers ranged from 1.04% to 5%. The ICCs for the total retinal thickness in the normal and diabetic mice were 0.78 [0.10, 0.92] and 0.83 [0.31, 0.96], respectively.
  
  
  CONCLUSIONS
  The presented method (publicly available as part of the Iowa Reference Algorithms) has acceptable accuracy and reproducibility and is expected to be useful in the quantitative study of intraretinal layers in mice.
  
  
  CONCLUSIONS
  The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well. Quantitative in vivo assessment of the retina in mice allows changes to be measured longitudinally, decreasing the need for them.
authors.affiliation: Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA, USA ; VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
authors.forename: Bhavna J
authors.initials: BJ
authors.lastname: Antony
authors.affiliation: Department of Ophthalmology, Dong-A University, College of Medicine and Medical Research Center, Busan, Korea ; Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
authors.forename: Woojin
authors.initials: W
authors.lastname: Jeong
authors.affiliation: Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA, USA ; VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA ; Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA ; Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.affiliation: Bioptigen Inc., Morrisville, NC, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Vance
authors.affiliation: Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
authors.forename: Elliott H
authors.initials: EH
authors.lastname: Sohn
authors.affiliation: Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA, USA ; VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
keywords: SD-OCT; automated segmentation; diabetes; mice