dc.contributor.author | López-Varela, Emilio | |
dc.contributor.author | Olivier Pascual, Nuria | |
dc.contributor.author | Quezada-Sánchez, Johnny | |
dc.contributor.author | Oreja-Guevara, Celia | |
dc.contributor.author | Barreira, Noelia | |
dc.date.accessioned | 2024-10-02T12:44:59Z | |
dc.date.available | 2024-10-02T12:44:59Z | |
dc.date.issued | 2025-02 | |
dc.identifier.citation | E. López-Varela, N. Olivier Pascual, J. Quezada-Sánchez, C. Oreja-Guevara, and N. Barreira, "Efficient semi-supervised hierarchical training for segmenting choroidal vessels and other structures on OCT images of multiple sclerosis patients", Biomedical Signal Processing and Control, Vol. 100, Part C, Feb. 2025, 106937, doi: 10.1016/j.bspc.2024.106937 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/39370 | |
dc.description.abstract | [Abstract]: Optical coherence tomography (OCT) is a non-invasive imaging technique used to diagnose ocular and systemic diseases. Recently, several clinical studies have linked changes in different ocular layers to the development of multiple sclerosis (MS), so accurate segmentation of these structures has become an essential task. Unfortunately, segmenting the entire set of structures involved is a very difficult task, due to their large number and variability. These two factors hinder the labeling of images and therefore severely restrict the ability to achieve a large dataset with all structures manually annotated, limiting the use of a standard supervised approach. In this paper, we propose a semi-supervised learning methodology to robustly segment ocular structures in OCT images using a limited number of partially labeled images. Our methodology maximizes the information we can extract from labeled images through hierarchical learning, where multiple decoders are used to extract segmented structures progressively. We use a reconstruction loss function to provide structural coherence to the segmentation and a teacher–student strategy to effectively leverage the information present in the set of unlabeled images. In addition to the segmentation of labeled structures, this hierarchical approach allows segmenting structures that are not labeled in the dataset such as the choroidal vessels. To validate the proposed methodology, we have carried out extensive experimentation using two datasets with different characteristics. These experiments have demonstrated a great potential of this methodology to train networks efficiently with partially labeled images, which allows to accurately extract the main biomarkers linked to the development of MS. | es_ES |
dc.description.sponsorship | This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, PDC2022-133132-I00 and TED2021-1312
01B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by ‘‘Consellería de Cultura, Educación e Universidade from Xunta de Galicia, Spain’’, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by ‘‘Secretaría Xeral de Universidades, Spain’’, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI, Spain Grant Program through PID2019-108435RB-I00 project. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | 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 2021-2024/PDC2022-133132-I00/ES/MEJORAS EN EL DIAGNÓSTICO E INVESTIGACIÓN CLÍNICO MEDIANTE TECNOLOGÍAS INTELIGENTES APLICADAS LA IMAGEN OFTALMOLÓGICA | 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/10.1016/j.bspc.2024.106937 | es_ES |
dc.rights | Attribution 4.0 International (CC BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Optical coherence tomography | es_ES |
dc.subject | Semi-supervised learning | es_ES |
dc.subject | Reconstruction loss | es_ES |
dc.subject | Teacher student network | es_ES |
dc.subject | Contrastive learning | es_ES |
dc.subject | Multiple sclerosis | es_ES |
dc.title | Efficient semi-supervised hierarchical training for segmenting choroidal vessels and other structures on OCT images of multiple sclerosis patients | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Biomedical Signal Processing and Control | es_ES |
UDC.volume | 100 | es_ES |
UDC.issue | Part C | es_ES |
UDC.startPage | 106937 | es_ES |
dc.identifier.doi | 10.1016/j.bspc.2024.106937 | |
UDC.coleccion | Investigación | |
UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | |
UDC.grupoInv | Grupo de Visión Artificial e Recoñecemento de Patróns (VARPA) | |