VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis

Autores
Feldman, Paula; Fainstein, Miguel; Siless, Viviana; Delrieux, Claudio; Iarussi, Emmanuel
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Vascular 3D model
Generative modeling
Neural Networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/178481

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network_name_str SEDICI (UNLP)
spelling VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel SynthesisFeldman, PaulaFainstein, MiguelSiless, VivianaDelrieux, ClaudioIarussi, EmmanuelCiencias InformáticasVascular 3D modelGenerative modelingNeural NetworksWe present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.Sociedad Argentina de Informática e Investigación Operativa2024-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf60-69http://sedici.unlp.edu.ar/handle/10915/178481enginfo:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17886info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:47:48Zoai:sedici.unlp.edu.ar:10915/178481Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:47:48.421SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
title VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
spellingShingle VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
Feldman, Paula
Ciencias Informáticas
Vascular 3D model
Generative modeling
Neural Networks
title_short VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
title_full VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
title_fullStr VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
title_full_unstemmed VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
title_sort VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
dc.creator.none.fl_str_mv Feldman, Paula
Fainstein, Miguel
Siless, Viviana
Delrieux, Claudio
Iarussi, Emmanuel
author Feldman, Paula
author_facet Feldman, Paula
Fainstein, Miguel
Siless, Viviana
Delrieux, Claudio
Iarussi, Emmanuel
author_role author
author2 Fainstein, Miguel
Siless, Viviana
Delrieux, Claudio
Iarussi, Emmanuel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Vascular 3D model
Generative modeling
Neural Networks
topic Ciencias Informáticas
Vascular 3D model
Generative modeling
Neural Networks
dc.description.none.fl_txt_mv We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
Sociedad Argentina de Informática e Investigación Operativa
description We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/178481
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/17886
info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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