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https://hdl.handle.net/1822/71250
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Campo DC | Valor | Idioma |
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dc.contributor.author | Oliveira, Americo | por |
dc.contributor.author | Pereira, Sergio | por |
dc.contributor.author | Silva, Carlos A. | por |
dc.date.accessioned | 2021-04-03T14:34:39Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Oliveira, A., Pereira, S., & Silva, C. A. (2018). Retinal vessel segmentation based on Fully Convolutional Neural Networks. Expert Systems with Applications, 112, 229-242. doi: https://doi.org/10.1016/j.eswa.2018.06.034 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://hdl.handle.net/1822/71250 | - |
dc.description.abstract | The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a multiscale Fully Convolutional Neural Network to cope with the varying width and direction of the vessel structure in the retina. Our proposal uses rotation operations as the basis of a joint strategy for both data augmentation and prediction, which allows us to explore the information learned during training to refine the segmentation. The method was evaluated on three publicly available databases, achieving an average accuracy of 0.9576, 0.9694, and 0.9653, and average area under the ROC curve of 0.9821, 0.9905, and 0.9855 on the DRIVE, STARE, and CHASE_DB1 databases, respectively. It also appears to be robust to the training set and to the inter-rater variability, which shows its potential for real-world applications. | por |
dc.description.sponsorship | The authors would like to thank the suggestions of the Anonymous Reviewers that helped to improve this document. This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER, Portugal funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalizacao (POCI) with the reference project POCI-01-0145-FEDER-006941. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.rights | restrictedAccess | por |
dc.subject | Fully Convolutional Neural Network | por |
dc.subject | Stationary Wavelet Transform | por |
dc.subject | Retinal fundus image | por |
dc.subject | Vessel segmentation | por |
dc.subject | Deep learning | por |
dc.title | Retinal vessel segmentation based on Fully Convolutional Neural Networks | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417418303816 | por |
oaire.citationStartPage | 229 | por |
oaire.citationEndPage | 242 | por |
oaire.citationVolume | 112 | por |
dc.identifier.doi | 10.1016/j.eswa.2018.06.034 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Médica | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Expert Systems with Applications | por |
oaire.version | CVoR | por |
dc.subject.ods | Saúde de qualidade | por |
Aparece nas coleções: | CMEMS - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Oliveira_Pereira_Silva@2018.pdf Acesso restrito! | 3,22 MB | Adobe PDF | Ver/Abrir |