ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-06042021-091824


Thesis type
Tesi di laurea magistrale
Author
GARZIA, SIMONE
URN
etd-06042021-091824
Thesis title
Automated 3D aortic segmentation of 4D flow MRI using deep learning
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA BIOMEDICA
Supervisors
relatore Prof. Positano, Vincenzo
supervisore Dott. Scarpolini, Martino Andrea
supervisore Prof.ssa Celi, Simona
Keywords
  • 4D flow
  • deep learning
  • segmentation
  • aorta
  • neural network
  • MRI
  • semantic segmentation
Graduation session start date
16/07/2021
Availability
Partial
Release date
16/07/2024
Summary
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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