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ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration
dc.contributor.author | Rivas-Villar, David | |
dc.contributor.author | Hervella, Álvaro S. | |
dc.contributor.author | Rouco, J. | |
dc.contributor.author | Novo Buján, Jorge | |
dc.date.accessioned | 2024-07-15T09:41:58Z | |
dc.date.available | 2024-07-15T09:41:58Z | |
dc.date.issued | 2024-07 | |
dc.identifier.citation | Rivas-Villar, D., Hervella, Á.S., Rouco, J. et al. ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03160-6 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/37987 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.description.abstract | [Abstract]: Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is supported by Ministerio de Ciencia e Innovación, Government of Spain, through the PID2019-108435RB-I00, TED2021-131201B-I00, and PDC2022-133132-I00 research projects; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva ref. ED431C 2020/24, predoctoral fellowship ref. ED481A 2021/147 and the postdoctoral fellowship ref. ED481B-2022-025; and Instituto de Salud Carlos III (ISCIII) under the grant FORT23/00010 as part of the Programa FORTALECE of Ministerio de Ciencia e Innovación. Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A 2021/147 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481B-2022-025 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | 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/TED2021-131201B-I00/ES/DIAGNÓSTICO DIGITAL: TRANSFORMACIÓN DE LA DETECCIÓN DE ENFERMEDADES NEUROVASCULARES Y DEL TRATAMIENTO DE LOS PACIENTES | 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/ISCII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/FORT23%2F00010/ES/ | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11517-024-03160-6 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Feature-based registration | es_ES |
dc.subject | Image registration | es_ES |
dc.subject | Medical imaging | es_ES |
dc.subject | Retinal image registration | es_ES |
dc.subject | Self-supervised learning | es_ES |
dc.title | ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Medical & biological engineering & computing | es_ES |
dc.identifier.doi | 10.1007/s11517-024-03160-6 |
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