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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/178481
Ver los metadatos del registro completo
id |
SEDICI_a112e74d08115c914f308984f1a8f979 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/178481 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
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 info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/178481 |
url |
http://sedici.unlp.edu.ar/handle/10915/178481 |
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) |
dc.format.none.fl_str_mv |
application/pdf 60-69 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616341092302848 |
score |
13.070432 |