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Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical ima...

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Autores principales: Camara, José, Rezende, Roberto, Pires, Ivan Miguel, Cunha, António
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267177/
https://www.ncbi.nlm.nih.gov/pubmed/35807135
http://dx.doi.org/10.3390/jcm11133850
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author Camara, José
Rezende, Roberto
Pires, Ivan Miguel
Cunha, António
author_facet Camara, José
Rezende, Roberto
Pires, Ivan Miguel
Cunha, António
author_sort Camara, José
collection PubMed
description Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.
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spelling pubmed-92671772022-07-09 Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing? Camara, José Rezende, Roberto Pires, Ivan Miguel Cunha, António J Clin Med Article Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis. MDPI 2022-07-02 /pmc/articles/PMC9267177/ /pubmed/35807135 http://dx.doi.org/10.3390/jcm11133850 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Camara, José
Rezende, Roberto
Pires, Ivan Miguel
Cunha, António
Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title_full Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title_fullStr Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title_full_unstemmed Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title_short Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?
title_sort retinal glaucoma public datasets: what do we have and what is missing?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267177/
https://www.ncbi.nlm.nih.gov/pubmed/35807135
http://dx.doi.org/10.3390/jcm11133850
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