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Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes

Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of...

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Autores principales: Baamonde, Sergio, de Moura, Joaquim, Novo, Jorge, Charlón, Pablo, Ortega, Marcos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929067/
https://www.ncbi.nlm.nih.gov/pubmed/31795480
http://dx.doi.org/10.3390/s19235269
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author Baamonde, Sergio
de Moura, Joaquim
Novo, Jorge
Charlón, Pablo
Ortega, Marcos
author_facet Baamonde, Sergio
de Moura, Joaquim
Novo, Jorge
Charlón, Pablo
Ortega, Marcos
author_sort Baamonde, Sergio
collection PubMed
description Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM’s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91.9%. Regarding the post-processing stage, mean specificity values of 91.9% and 99.0% were obtained from volumes with and without the ERM’s presence, respectively.
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spelling pubmed-69290672019-12-26 Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes Baamonde, Sergio de Moura, Joaquim Novo, Jorge Charlón, Pablo Ortega, Marcos Sensors (Basel) Article Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM’s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91.9%. Regarding the post-processing stage, mean specificity values of 91.9% and 99.0% were obtained from volumes with and without the ERM’s presence, respectively. MDPI 2019-11-29 /pmc/articles/PMC6929067/ /pubmed/31795480 http://dx.doi.org/10.3390/s19235269 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Baamonde, Sergio
de Moura, Joaquim
Novo, Jorge
Charlón, Pablo
Ortega, Marcos
Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title_full Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title_fullStr Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title_full_unstemmed Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title_short Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
title_sort automatic identification and intuitive map representation of the epiretinal membrane presence in 3d oct volumes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929067/
https://www.ncbi.nlm.nih.gov/pubmed/31795480
http://dx.doi.org/10.3390/s19235269
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