Comparison of UNet and DC-UNet models for an efficient segmentation and visualization of rodent hepatic vascular network from X-ray phase contrast imaging

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSigma Clermont Institut Pascal-
Autor(es): dc.creatorAlvarez, Matheus-
Autor(es): dc.creatorPina, Diana-
Autor(es): dc.creatorRositi, Hugo-
Autor(es): dc.creatorVacavant, Antoine-
Data de aceite: dc.date.accessioned2025-08-21T17:06:48Z-
Data de disponibilização: dc.date.available2025-08-21T17:06:48Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ISBI53787.2023.10230779-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308630-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308630-
Descrição: dc.descriptionThis study proposes deep neural methods and tools for the extraction and visualization of vascular systems, through SR-PCT images (synchrotron radiation X-ray phase-contrast tomography) of murine liver. This is the first time that two deep learning architectures with different parametrizations were applied and compared for vessel segmentation with this imaging modality. Moreover, we propose to apply pre-processing steps (CLAHE, sigmoid and Gaussian filtering) in order to improve the contrast of raw data. We show that the best performance is obtained thanks to a DC-UNet model, learnt with these improved images. With this complete pipeline, we were able to segment and visualize in 3D the complete liver vasculature within a volume of more than 10003 voxels.-
Descrição: dc.descriptionCentre of Medical Physics and Radiological Protection Botucatu Clinical Hospital Unesp-
Descrição: dc.descriptionUniversité Clermont Auvergne Cnrs Sigma Clermont Institut Pascal-
Descrição: dc.descriptionCentre of Medical Physics and Radiological Protection Botucatu Clinical Hospital Unesp-
Idioma: dc.languageen-
Relação: dc.relationProceedings - International Symposium on Biomedical Imaging-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectImage processing-
Palavras-chave: dc.subjectImage segmentation-
Palavras-chave: dc.subjectVessel segmentation-
Palavras-chave: dc.subjectX-ray phase contrast imaging-
Título: dc.titleComparison of UNet and DC-UNet models for an efficient segmentation and visualization of rodent hepatic vascular network from X-ray phase contrast imaging-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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