Tesi etd-03302021-105400 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
LONGO, SERGIO
URN
etd-03302021-105400
Titolo
Graph Neural Networks as surrogate for thoracic aortic aneurysm wall shear stress estimation
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Ing. Positano, Vincenzo
relatore Ing. Celi, Simona
relatore Dott. Scarpolini, Martino Andrea
relatore Ing. Celi, Simona
relatore Dott. Scarpolini, Martino Andrea
Parole chiave
- aorta
- artificial intelligence
- deep neural network
- graph neural network
- thoracic aortic aneurysm
- wall shear stress
Data inizio appello
23/04/2021
Consultabilità
Completa
Riassunto
By representing mesh as a graph, it is possible to apply graph convolution to extract features and preserving the spatial coherency. In our work, we focused in the prediction of wall shear stress distribution directly over 3D surfaces. Each surface represent a thoracic aorta affected by aneurysm. All the geometries used come from real segmentations.
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Master_T...Longo.pdf | 10.02 Mb |
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