ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06042021-091824


Tipo di tesi
Tesi di laurea magistrale
Autore
GARZIA, SIMONE
URN
etd-06042021-091824
Titolo
Automated 3D aortic segmentation of 4D flow MRI using deep learning
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof. Positano, Vincenzo
supervisore Dott. Scarpolini, Martino Andrea
supervisore Prof.ssa Celi, Simona
Parole chiave
  • 4D flow
  • deep learning
  • segmentation
  • aorta
  • neural network
  • MRI
  • semantic segmentation
Data inizio appello
16/07/2021
Consultabilità
Non consultabile
Data di rilascio
16/07/2024
Riassunto
The main objective of this thesis, carried out at the BioCardioLab of the Foundation Gabriele Monasterio in Massa, is to realize a deep neural network that is able to fully automate the 3D segmentation of the aorta from 4D Flow PCMRA images.
The starting point of this work are the 3D volumes generated by the 4D Flow MRI acquisition and divided into 4 datasets: one of 'magnitude' and the three time-resolved velocity datasets along the three spatial directions. They are processed and used to obtain a single 3D PCMRA dataset. In the course of this thesis work, multiple network architectures, based on the classical U-net architecture, and optimization strategies have been experimented.
In particular, the performance of a 3D and a 2D network has been compared on the same test dataset.
Several network performance optimization strategies were used, such as Data Augmentation on volumetric images and the creation of two-channel PCMRA volumes that exploit the information derived from the systolic and diastolic phases of the cardiac cycle for a better visibility of cardiac structures.
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