Two-dimensional segmentation of the retinal vascular network from optical coherence tomography
Autor(a) principal: | |
---|---|
Data de Publicação: | 2013 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.2/3909 |
Resumo: | The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results. |
id |
RCAP_b8006e46b96848388924591ebfb9118c |
---|---|
oai_identifier_str |
oai:repositorioaberto.uab.pt:10400.2/3909 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomographyThe automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.Repositório AbertoRodrigues, PedroGuimarães, PedroSantos, TorcatoSimão, SílviaMiranda, TelmoSerranho, PedroBernardes, Rui2015-04-30T14:26:24Z20132015-02-25T16:48:37Z2013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.2/3909porRodrigues, Pedro [et al.] - Two-dimensional segmentation of the retinal vascular network from optical coherence tomography. "Journal of Biomedical Optics" [Em linha]. ISSN 1083-3668 (Print) 1560-2281 (Online). Vol. 18 nº 12 (2013), 12 p.1083-36681560-2281info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T15:19:04Zoai:repositorioaberto.uab.pt:10400.2/3909Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:44:56.952934Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
title |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
spellingShingle |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography Rodrigues, Pedro |
title_short |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
title_full |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
title_fullStr |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
title_full_unstemmed |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
title_sort |
Two-dimensional segmentation of the retinal vascular network from optical coherence tomography |
author |
Rodrigues, Pedro |
author_facet |
Rodrigues, Pedro Guimarães, Pedro Santos, Torcato Simão, Sílvia Miranda, Telmo Serranho, Pedro Bernardes, Rui |
author_role |
author |
author2 |
Guimarães, Pedro Santos, Torcato Simão, Sílvia Miranda, Telmo Serranho, Pedro Bernardes, Rui |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Aberto |
dc.contributor.author.fl_str_mv |
Rodrigues, Pedro Guimarães, Pedro Santos, Torcato Simão, Sílvia Miranda, Telmo Serranho, Pedro Bernardes, Rui |
description |
The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 2013-01-01T00:00:00Z 2015-04-30T14:26:24Z 2015-02-25T16:48:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.2/3909 |
url |
http://hdl.handle.net/10400.2/3909 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Rodrigues, Pedro [et al.] - Two-dimensional segmentation of the retinal vascular network from optical coherence tomography. "Journal of Biomedical Optics" [Em linha]. ISSN 1083-3668 (Print) 1560-2281 (Online). Vol. 18 nº 12 (2013), 12 p. 1083-3668 1560-2281 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799135020248465408 |