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Vessel Tree Extraction and Depth Estimation with OCT Images
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.contributor.author | Barreira, Noelia | |
dc.contributor.author | Penedo, Manuel | |
dc.date.accessioned | 2024-06-05T18:13:42Z | |
dc.date.available | 2024-06-05T18:13:42Z | |
dc.date.issued | 2016-09-08 | |
dc.identifier.citation | de Moura, J., Novo, J., Ortega, M., Barreira, N., Penedo, M.G. (2016). Vessel Tree Extraction and Depth Estimation with OCT Images. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_3 | es_ES |
dc.identifier.isbn | 978-3-319-44635-6 | |
dc.identifier.isbn | 978-3-319-44636-3 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | http://hdl.handle.net/2183/36814 | |
dc.description | The conference was held in Salamanca, Spain, in September 2016. | es_ES |
dc.description.abstract | [Absctract]: The identification of the retinal arterio-venular tree is a relevant issue for its analysis in a large variability of procedures. Classical methodologies employ 2D acquisition strategies that obtain a limited representation of the vascular structure. This paper proposes a new methodology for 2D vessel tree extraction and the corresponding depth estimation using Optical Coherence Tomography (OCT) images. This way, the proposal defines a more complete scenario for an adequate posterior vasculature analysis. The methodology employs different image analysis techniques to initially extract the 2D vessel tree. Then, the method maps these 2D positions in the corresponding histological sections of the OCT images and estimates the corresponding depths along all the vessel tree. To test and validate this proposal, this work employed 196 OCT histological images with the corresponding near infrared reflectance retinographies. The methodology provided promising results, indicating an acceptable accuracy in a complex domain as is the vessel tree identification. It provides a coherent 2D vessel tree extraction with the corresponding depth estimations that constitute a scenario with high potentially useful information for posterior medical analysis and diagnostic processes of many diseases as, for example, hypertension or diabetes. | es_ES |
dc.description.sponsorship | This work is supported by the Instituto de Salud Car- los III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PI14%2F02161/ES/DESARROLLO DE UN SISTEMA AUTOMÁTICO PARA EL CÁLCULO Y VISUALIZACIÓN DE PROPIEDADES ANATÓMICAS DE LA RETINA EN SD-OCT Y SU CORRELACIÓN CON ANÁLISIS FUNCIONALES HETEROGÉNEOS DE LA VISIÓN | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DTS15%2F00153/ES/SIRIUS - SISTEMA DE ANÁLISIS DE MICROCIRCULACIÓN RETINIANA: EVALUACIÓN MULTIDISCIPLINAR E INTEGRACIÓN EN PROTOCOLOS CLÍNICOS | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2015-69948-R/ES/IDENTIFICACION Y CARACTERIZACION DEL EDEMA MACULAR DIABETICO MEDIANTE ANALISIS AUTOMATICO DE TOMOGRAFIAS DE COHERENCIA OPTICA Y TECNICAS DE APRENDIZAJE MAQUINA | es_ES |
dc.relation.uri | https://doi.org/10.1007/978-3-319-44636-3_3 | es_ES |
dc.rights | © 2016 Springer Nature | es_ES |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | Retinal imaging | es_ES |
dc.subject | OCT | es_ES |
dc.subject | Vessel tree | es_ES |
dc.title | Vessel Tree Extraction and Depth Estimation with OCT Images | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Advances in Artificial Intelligence: 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Spain, September 14-16, 2016. Proceedings (Lecture Notes in Computer Science) | es_ES |
UDC.volume | 9868 | es_ES |
UDC.startPage | 23 | es_ES |
UDC.endPage | 33 | es_ES |
UDC.conferenceTitle | 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016) | es_ES |