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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6929067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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