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Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images
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 selecte...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561132/ https://www.ncbi.nlm.nih.gov/pubmed/31211004 http://dx.doi.org/10.1167/tvst.8.3.39 |
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author | Elsawy, Amr Abdel-Mottaleb, Mohamed Sayed, Ibrahim-Osama Wen, Dan Roongpoovapatr, Vatookarn Eleiwa, Taher Sayed, Ahmed M. Raheem, Mariam Gameiro, Gustavo Shousha, Mohamed Abou |
author_facet | Elsawy, Amr Abdel-Mottaleb, Mohamed Sayed, Ibrahim-Osama Wen, Dan Roongpoovapatr, Vatookarn Eleiwa, Taher Sayed, Ahmed M. Raheem, Mariam Gameiro, Gustavo Shousha, Mohamed Abou |
author_sort | Elsawy, Amr |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6561132 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-65611322019-06-17 Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images Elsawy, Amr Abdel-Mottaleb, Mohamed Sayed, Ibrahim-Osama Wen, Dan Roongpoovapatr, Vatookarn Eleiwa, Taher Sayed, Ahmed M. Raheem, Mariam Gameiro, Gustavo Shousha, Mohamed Abou Transl Vis Sci Technol Articles 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. The Association for Research in Vision and Ophthalmology 2019-06-11 /pmc/articles/PMC6561132/ /pubmed/31211004 http://dx.doi.org/10.1167/tvst.8.3.39 Text en Copyright 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Articles Elsawy, Amr Abdel-Mottaleb, Mohamed Sayed, Ibrahim-Osama Wen, Dan Roongpoovapatr, Vatookarn Eleiwa, Taher Sayed, Ahmed M. Raheem, Mariam Gameiro, Gustavo Shousha, Mohamed Abou Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title | Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title_full | Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title_fullStr | Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title_full_unstemmed | Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title_short | Automatic Segmentation of Corneal Microlayers on Optical Coherence Tomography Images |
title_sort | automatic segmentation of corneal microlayers on optical coherence tomography images |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561132/ https://www.ncbi.nlm.nih.gov/pubmed/31211004 http://dx.doi.org/10.1167/tvst.8.3.39 |
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