Deep Convolutional Neural Networks for Segmenting Unruptured Intracranial Aneurysms from 3D TOF-MRA Images

Despite facing technical issues (e.g., overfitting, vanishing and exploding gradients), deep neural networks have the potential to capture complex patterns in data. Understanding how depth impacts neural networks performance is vital to the advancement of novel deep learning architectures. By varying hyperparameters on two sets of architectures with different depths, this thesis aims to examine if there are any potential benefits from developing deep networks for segmenting intracranial aneurysms from 3D TOF-MRA scans in the ADAM dataset.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Boonaneksap, Surasith

Chair dc:contributor.committeechair

• Jones, Creed F. III

Committee members dc:contributor.committeemember

• Jia, Ruoxi
• Lourentzou, Ismini

• Convolutional Neural Networks
• Intracranial Aneurysms
• Image Segmentation

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:33858

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/108224