Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100824
DC FieldValueLanguage
dc.contributor.authorCazanas-Gordon, Alex-
dc.contributor.authorParra-Mora, Esther-
dc.contributor.authorCruz, Luis Alberto da Silva-
dc.date.accessioned2022-07-13T10:40:09Z-
dc.date.available2022-07-13T10:40:09Z-
dc.date.issued2021-
dc.identifier.issn2169-3536pt
dc.identifier.urihttp://hdl.handle.net/10316/100824-
dc.description.abstractManual assessment of the retinal thickness in optical coherence tomography images is a timeconsuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The dif culty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets that included cases of severe retinal edema. Moreover, the performance of the proposed method was compared to two existing methods, widely referenced in the relevant literature. The proposed algorithm outperformed reference methods at segmenting the retinal boundaries in both normal and pathological images. Furthermore, a thorough reliability analysis showed a strong agreement between the retinal thickness measurements derived from the segmentation obtained with the proposed method and corresponding manual measurements computed with the manual annotations.pt
dc.language.isoengpt
dc.relationFCT UIDB/EEA/50008/2020pt
dc.relationSecretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuadorpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectDeep learningpt
dc.subjectensemble learningpt
dc.subjectsemantic segmentationpt
dc.subjectimage processingpt
dc.subjectretinal thicknesspt
dc.subjectoptical coherence tomographypt
dc.titleEnsemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomographypt
dc.typearticle-
degois.publication.firstPage67349pt
degois.publication.lastPage67363pt
degois.publication.titleIEEE Accesspt
dc.peerreviewedyespt
dc.identifier.doi10.1109/ACCESS.2021.3076427pt
degois.publication.volume9pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitIT - Institute of Telecommunications-
crisitem.author.orcid0000-0002-4597-6328-
crisitem.author.orcid0000-0002-9008-031X-
crisitem.author.orcid0000-0003-1141-4404-
Appears in Collections:I&D IT - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais
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This item is licensed under a Creative Commons License Creative Commons