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Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images
dc.contributor.author | Hervella, Álvaro S. | |
dc.contributor.author | Rouco, J. | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Penedo, Manuel | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2023-12-13T15:54:19Z | |
dc.date.available | 2023-12-13T15:54:19Z | |
dc.date.issued | 2020-04 | |
dc.identifier.citation | Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images. Computer Methods and Programs in Biomedicine, 186(105201), 105201. doi:10.1016/j.cmpb.2019.105201 | es_ES |
dc.identifier.issn | 0169-2607 | |
dc.identifier.uri | http://hdl.handle.net/2183/34481 | |
dc.description | ©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). “Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 186(105201), 105201. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2019.105201. | es_ES |
dc.description.abstract | [Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images. Method: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks. Results: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin. Conclusions: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions. | es_ES |
dc.description.sponsorship | This work is supported by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project, and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. The authors of this work also receive financial support from the ERDF and European Social Fund (ESF) of the EU, and Xunta de Galicia through Centro Singular de Investigación de Galicia, accreditation 2016–2019, ref. ED431G/01, Grupo de Referencia Competitiva, ref. ED431C 2016-047, and the predoctoral grant contract ref. ED481A-2017/328. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A-2017/328 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica | 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 | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.cmpb.2019.105201 | |
dc.relation.uri | https://doi.org/10.1016/j.cmpb.2019.105201 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Deep learning | es_ES |
dc.subject | Eye fundus | es_ES |
dc.subject | Blood vessels | es_ES |
dc.subject | Crossings | es_ES |
dc.subject | Bifurcations | es_ES |
dc.subject | Landmark detection | es_ES |
dc.title | Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Computer Methods and Programs in Biomedicine | es_ES |
UDC.volume | 186 | es_ES |
UDC.startPage | 105201 | es_ES |
dc.identifier.doi | 10.1016/j.cmpb.2019.105201 |
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