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Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios
dc.contributor.author | Gende, M. | |
dc.contributor.author | Mallén Gracia, Victor | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Cordón Ciordia, Beatriz | |
dc.contributor.author | García-Martín, Elena | |
dc.contributor.author | Sánchez, Clara I. | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-08T07:49:14Z | |
dc.date.available | 2024-05-08T07:49:14Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | M. Gende et al., «Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios», IEEE J. Biomed. Health Inform., vol. 27, n.o 11, pp. 5483-5494, nov. 2023, doi: 10.1109/JBHI.2023.3313392. | es_ES |
dc.identifier.issn | 2168-2194 | |
dc.identifier.issn | 2168-2208 | |
dc.identifier.uri | http://hdl.handle.net/2183/36433 | |
dc.description | Open Access provided by ‘Universidade da Coruña/CISUG’ within the CRUI-CARE Agreement | es_ES |
dc.description.abstract | [Absctract]: Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND. | es_ES |
dc.description.sponsorship | This work was supported in part by Instituto de Salud Carlos III, Government of Spain, under Grants PI17/01726 and PI20/00437, in part by Inflammatory Disease Network (RICORS) under Grant RD21/0002/0050, in part by the Ministerio de Ciencia e Innovación, Government of Spain under Grants PDC2022-133132-I00, RTI2018-095894-B-I00, PID2019- 108435RB-I00, and TED2021-131201B-I00, in part by the Consellería de Cultura, Educación e Universidade, Xunta de Galicia through Grupos de Referencia Competitiva under Grant ED431C 2020/24 and predoctoral under Grant ED481A 2021/161, in part by CITIC, Centro de Investigación de Galicia under Grant ED431G 2019/01, and in part by Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia through the ERDF (80%) and Secretaría Xeral de Universidades (20%). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI17%2F01726/ES/EVALUACIÓN NEUROOFTALMOLÓGICA COMO BIOMARCADOR DIAGNÓSTICO, EVOLUTIVO Y PRONÓSTICO EN EL CURSO DE LA ESCLEROSIS MÚLTIPLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI20%2F00437/ES/LA NEURORRETINA COMO BIOMARCADOR PRECOZ Y DE PROGRESION DESDE DETERIORO COGNITIVO LEVE A ALZHEIMER Y EFECTO PROTECTOR DE LA REHABILITACION COGNITIVO-VISUAL EN LA PROGRESION DE LA DEMENCIA | 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 | info: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 MÚLTIPLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-131201B-I00/ES/DIAGNÓSTICO DIGITAL: TRANSFORMACIÓN DE LA DETECCIÓN DE ENFERMEDADES NEUROVASCULARES Y DEL TRATAMIENTO DE LOS PACIENTES | es_ES |
dc.relation.uri | https://doi.org/10.1109/jbhi.2023.3313392 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Deep learning | es_ES |
dc.subject | Medical image segmentation | es_ES |
dc.subject | Neurological disease | es_ES |
dc.subject | Optical coherence tomography | es_ES |
dc.subject | Retina | es_ES |
dc.title | Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | IEEE Journal of Biomedical and Health Informatics | es_ES |
UDC.volume | 27 | es_ES |
UDC.issue | 11 | es_ES |
UDC.startPage | 5483 | es_ES |
UDC.endPage | 5494 | es_ES |
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