VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis

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 st...

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Detalles Bibliográficos
Autores principales: Feldman, Paula, Fainstein, Miguel, Siless, Viviana, Delrieux, Claudio, Iarussi, Emmanuel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
Vascular 3D model
Generative modeling
Neural Networks
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/178481
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata

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