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dc.contributor.authorLópez-Varela, Emilio
dc.contributor.authorBarreira, Noelia
dc.contributor.authorOlivier Pascual, Nuria
dc.contributor.authorArroyo Castillo, Maria Rosa
dc.contributor.authorPenedo, Manuel
dc.date.accessioned2024-02-01T08:42:56Z
dc.date.available2024-02-01T08:42:56Z
dc.date.issued2023-09
dc.identifier.citationE. López-Varela, N. Barreira, N. O. Pascual, M. R. A. Castillo, y M. G. Penedo, «Generation of synthetic intermediate slices in 3D OCT cubes for improving pathology detection and monitoring», Computers in Biology and Medicine, vol. 163, p. 107214, sep. 2023, doi: 10.1016/j.compbiomed.2023.107214.es_ES
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.urihttp://hdl.handle.net/2183/35308
dc.descriptionFunding for open access charge: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Absctract]: OCT is a non-invasive imaging technique commonly used to obtain 3D volumes of the ocular structure. These volumes allow the monitoring of ocular and systemic diseases through the observation of subtle changes in the different structures present in the eye. In order to observe these changes it is essential that the OCT volumes have a high resolution in all axes, but unfortunately there is an inverse relationship between the quality of the OCT images and the number of slices of the cube. This results in routine clinical examinations using cubes that generally contain high-resolution images with few slices. This lack of slices complicates the monitoring of changes in the retina hindering the diagnostic process and reducing the effectiveness of 3D visualizations. Therefore, increasing the cross-sectional resolution of OCT cubes would improve the visualization of these changes aiding the clinician in the diagnostic process. In this work we present a novel fully automatic methodology to perform the synthesis of intermediate slices of OCT image volumes in an unsupervised manner. To perform this synthesis, we propose a fully convolutional neural network architecture that uses information from two adjacent slices to generate the intermediate synthetic slice. We also propose a training methodology, where we use three adjacent slices to train the network by contrastive learning and image reconstruction. We test our methodology with three different types of OCT volumes commonly used in the clinical setting and validate the quality of the synthetic slices created with several medical experts and using an expert system.es_ES
dc.description.sponsorshipThis research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the postdoctoral, grant ref. ED481B-2021-059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruña/CISUG .es_ES
dc.description.sponsorshipXunta de Galicia; ED481B-2021-059es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/24es_ES
dc.description.sponsorshipXunta de Galicia; IN845D 2020/38es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherElsevier B.V.es_ES
dc.relationinfo: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 clínicaes_ES
dc.relationinfo: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 OFTALMOLOGICAes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLEes_ES
dc.relation.urihttps://doi.org/10.1016/j.compbiomed.2023.107214es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectOptical coherence tomographyes_ES
dc.subjectMedical synthesises_ES
dc.subjectSlice synthesises_ES
dc.subjectConvolutional neural networkses_ES
dc.subjectGANes_ES
dc.subject3D volumees_ES
dc.subjectOCTes_ES
dc.subjectResolution increasees_ES
dc.titleGeneration of synthetic intermediate slices in 3D OCT cubes for improving pathology detection and monitoringes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleComputers in Biology and Medicinees_ES
UDC.volume163es_ES
UDC.startPage107214es_ES


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