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VNR-AV: Structural Post-processing for Retinal Arteries and Veins Segmentation

dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorDULAU, Idris
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRECUR, Benoit
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDELCOURT, Cecile
ORCID: 0000-0002-2099-0481
IDREF: 035105291
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBEURTON AIMAR, Marie
IDREF: 122639839
dc.date.accessioned2024-11-20T14:11:05Z
dc.date.available2024-11-20T14:11:05Z
dc.date.issued2024-10-10
dc.date.conference2024-10-10
dc.identifier.isbn978-3031731181en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urioai:crossref.org:10.1007/978-3-031-73119-8_3
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/203373
dc.descriptionHeld in Conjunction with MICCAI 2024en_US
dc.description.abstractEnThe retinal vasculature reveals numerous health conditions, making the quantitative assessment of changes in retinal arteries and veins crucial for disease prevention and management. Quantifying changes in the retinal vasculature requires segmentation to delineate it. Deep-learning techniques demonstrate impressive results for retinal vasculature segmentation in color fundus images. However, even if the generated segmentations are good at the pixel level, they are not coherent at the structural level, (i.e. not anatomically coherent compared to a real retinal vasculature). The vasculature of the retina is composed of two completely connected trees: arteries and veins, whereas segmentations produce several disconnected components. In this article, we propose VNR-AV: a Vasculature Network Retrieval method specifically designed for retinal Arteries and Veins segmentation. The proposed post-processing method achieves two main objectives: it leverages vessels segmentation to enhance the segmentation of arteries and veins by performing reconnection, removal, and detail gathering; and it removes or reconnects segmentation components based on a set of rules developed through an understanding of deep-learning-generated segmentations. VNR-AV retrieve a fully connected thus more anatomically coherent structure of the retinal arteries and veins networks while managing to slightly improve the superposition quality at pixel-level. VNR-AV enable a more coherent assessment of changes in retinal arteries and veins and pave the way for further research in prevention and management of eye-related diseases.
dc.language.isoENen_US
dc.publisherSpringer Nature Switzerlanden_US
dc.sourcecrossref
dc.title.enVNR-AV: Structural Post-processing for Retinal Arteries and Veins Segmentation
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.1007/978-3-031-73119-8_3en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.page22-31en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.conference.title11th International Workshop, OMIA 2024en_US
bordeaux.countrymaen_US
bordeaux.title.proceedingOphthalmic Medical Image Analysis: 11th International Workshop, OMIA 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedingsen_US
bordeaux.teamLEHA_BPHen_US
bordeaux.conference.cityMarrakechen_US
bordeaux.import.sourcedissemin
hal.identifierhal-04793537
hal.version1
hal.date.transferred2024-11-20T14:11:08Z
hal.proceedingsouien_US
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2024-10-10&rft.spage=22-31&rft.epage=22-31&rft.eissn=0302-9743&rft.issn=0302-9743&rft.au=DULAU,%20Idris&RECUR,%20Benoit&HELMER,%20Catherine&DELCOURT,%20Cecile&BEURTON%20AIMAR,%20Marie&rft.isbn=978-3031731181&rft.genre=unknown


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