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Deformable registration of multimodal retinal images using a weakly supervised deep learning approach

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dc.contributor.authorMartínez Río, Javier
dc.contributor.authorCarmona, Enrique J.
dc.contributor.authorCancelas, Daniel
dc.contributor.authorNovo, Jorge
dc.contributor.authorOrtega, Marcos
dc.date.accessioned2024-06-11T15:15:26Z
dc.date.available2024-06-11T15:15:26Z
dc.date.issued2023-03-28
dc.description.abstractThere are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since each of them provides common and complementary visual information. However, if we want to facilitate the comparison of two images, obtained with different techniques and containing the same retinal region of interest, it will be necessary to make a previous registration of both images. Here, we present a weakly supervised deep learning methodology for robust deformable registration of multimodal retinal images, which is applied to implement a method for the registration of fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. This methodology is strongly inspired by VoxelMorph, a general unsupervised deep learning framework of the state of the art for deformable registration of unimodal medical images. The method was evaluated in a public dataset with 172 pairs of FA and superficial plexus OCTA images. The degree of alignment of the common information (blood vessels) and preservation of the non-common information (image background) in the transformed image were measured using the Dice coefficient (DC) and zero-normalized cross-correlation (ZNCC), respectively. The average values of the mentioned metrics, including the standard deviations, were DC = 0.72 ± 0.10 and ZNCC = 0.82 ± 0.04. The time required to obtain each pair of registered images was 0.12 s. These results outperform rigid and deformable registration methods with which our method was compared.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1007/s00521-023-08454-8
dc.identifier.issn1433-3058
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22404
dc.journal.titleNeural Computing and Applications
dc.journal.volume35
dc.language.isoen
dc.publisherSpringer
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsMultimodal image registration
dc.subject.keywordsDiffeomorphic transformation
dc.subject.keywordsDeep learning
dc.subject.keywordsVoxelMorph
dc.subject.keywordsOCT angiography
dc.subject.keywordsFluorescein angiography
dc.titleDeformable registration of multimodal retinal images using a weakly supervised deep learning approaches
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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