TY - JOUR
T1 - Automatic segmentation of corneal microlayers on optical coherence tomography images
AU - Elsawy, Amr
AU - Abdel-Mottaleb, Mohamed
AU - Sayed, Ibrahim Osama
AU - Wen, Dan
AU - Roongpoovapatr, Vatookarn
AU - Eleiwa, Taher
AU - Sayed, Ahmed M.
AU - Raheem, Mariam
AU - Gameiro, Gustavo
AU - Shousha, Mohamed Abou
N1 - Funding Information:
Supported by a National Eye Institute (NEI) K23 award (K23EY026118), NEI core center grant to the University of Miami (P30 EY014801), and Research to Prevent Blindness. The funding organization had no role in the design or conduct of this research.
Publisher Copyright:
© 2019, Association for Research in Vision and Ophthalmology Inc.. All rights reserved.
PY - 2019/5
Y1 - 2019/5
N2 - Purpose: To propose automatic segmentation algorithm (AUS) for corneal microlayers on optical coherence tomography (OCT) images. Methods: Eighty-two corneal OCT scans were obtained from 45 patients with normal and abnormal corneas. Three testing data sets totaling 75 OCT images were randomly selected. Initially, corneal epithelium and endothelium microlayers are estimated using a corneal mask and locally refined to obtain final segmentation. Flat-epithelium and flat-endothelium images are obtained and vertically projected to locate inner corneal microlayers. Inner microlayers are estimated by translating epithelium and endothelium microlayers to detected locations then refined to obtain final segmentation. Images were segmented by trained manual operators (TMOs) and by the algorithm to assess repeatability (i.e., intraoperator error), reproducibility (i.e., interoperator and segmentation errors), and running time. A random masked subjective test was conducted by corneal specialists to subjectively grade the segmentation algorithm. Results: Compared with the TMOs, the AUS had significantly less mean intraoperator error (0.53 ± 1.80 vs. 2.32 ± 2.39 pixels; P < 0.0001), it had significantly different mean segmentation error (3.44 ± 3.46 vs. 2.93 ± 3.02 pixels; P < 0.0001), and it had significantly less running time per image (0.19 ± 0.07 vs. 193.95 ± 194.53 seconds; P < 0.0001). The AUS had insignificant subjective grading for microlayer-segmentation grading (4.94 ± 0.32 vs. 4.96 ± 0.24; P = 0.5081), but it had significant subjective grading for regional-segmentation grading (4.96 ± 0.26 vs. 4.79 ± 0.60; P = 0.025). Conclusions: The AUS can reproduce the manual segmentation of corneal microlayers with comparable accuracy in almost real-time and with significantly better repeatability. Translational Relevance: The AUS can be useful in clinical settings and can aid the diagnosis of corneal diseases by measuring thickness of segmented corneal microlayers.
AB - Purpose: To propose automatic segmentation algorithm (AUS) for corneal microlayers on optical coherence tomography (OCT) images. Methods: Eighty-two corneal OCT scans were obtained from 45 patients with normal and abnormal corneas. Three testing data sets totaling 75 OCT images were randomly selected. Initially, corneal epithelium and endothelium microlayers are estimated using a corneal mask and locally refined to obtain final segmentation. Flat-epithelium and flat-endothelium images are obtained and vertically projected to locate inner corneal microlayers. Inner microlayers are estimated by translating epithelium and endothelium microlayers to detected locations then refined to obtain final segmentation. Images were segmented by trained manual operators (TMOs) and by the algorithm to assess repeatability (i.e., intraoperator error), reproducibility (i.e., interoperator and segmentation errors), and running time. A random masked subjective test was conducted by corneal specialists to subjectively grade the segmentation algorithm. Results: Compared with the TMOs, the AUS had significantly less mean intraoperator error (0.53 ± 1.80 vs. 2.32 ± 2.39 pixels; P < 0.0001), it had significantly different mean segmentation error (3.44 ± 3.46 vs. 2.93 ± 3.02 pixels; P < 0.0001), and it had significantly less running time per image (0.19 ± 0.07 vs. 193.95 ± 194.53 seconds; P < 0.0001). The AUS had insignificant subjective grading for microlayer-segmentation grading (4.94 ± 0.32 vs. 4.96 ± 0.24; P = 0.5081), but it had significant subjective grading for regional-segmentation grading (4.96 ± 0.26 vs. 4.79 ± 0.60; P = 0.025). Conclusions: The AUS can reproduce the manual segmentation of corneal microlayers with comparable accuracy in almost real-time and with significantly better repeatability. Translational Relevance: The AUS can be useful in clinical settings and can aid the diagnosis of corneal diseases by measuring thickness of segmented corneal microlayers.
KW - Corneal microlayers
KW - OCT imaging
KW - Segmentation
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U2 - 10.1167/tvst.8.3.39
DO - 10.1167/tvst.8.3.39
M3 - Article
AN - SCOPUS:85070819318
VL - 8
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
SN - 2164-2591
IS - 3
M1 - 39
ER -