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Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Do...

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Detalles Bibliográficos
Autores principales: Chiu, Stephanie J., Li, Xiao T., Nicholas, Peter, Toth, Cynthia A., Izatt, Joseph A., Farsiu, Sina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Optical Society of America 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408910/
https://www.ncbi.nlm.nih.gov/pubmed/20940837
http://dx.doi.org/10.1364/OE.18.019413
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author Chiu, Stephanie J.
Li, Xiao T.
Nicholas, Peter
Toth, Cynthia A.
Izatt, Joseph A.
Farsiu, Sina
author_facet Chiu, Stephanie J.
Li, Xiao T.
Nicholas, Peter
Toth, Cynthia A.
Izatt, Joseph A.
Farsiu, Sina
author_sort Chiu, Stephanie J.
collection PubMed
description Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.
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spelling pubmed-34089102012-10-01 Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation Chiu, Stephanie J. Li, Xiao T. Nicholas, Peter Toth, Cynthia A. Izatt, Joseph A. Farsiu, Sina Opt Express Research-Article Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. Optical Society of America 2010-08-27 /pmc/articles/PMC3408910/ /pubmed/20940837 http://dx.doi.org/10.1364/OE.18.019413 Text en ©2010 Optical Society of America http://creativecommons.org/licenses/by-nc-nd/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially.
spellingShingle Research-Article
Chiu, Stephanie J.
Li, Xiao T.
Nicholas, Peter
Toth, Cynthia A.
Izatt, Joseph A.
Farsiu, Sina
Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title_full Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title_fullStr Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title_full_unstemmed Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title_short Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
title_sort automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation
topic Research-Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408910/
https://www.ncbi.nlm.nih.gov/pubmed/20940837
http://dx.doi.org/10.1364/OE.18.019413
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