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

dc.contributor.authorBoonaneksap, Surasithen
dc.contributor.committeechairJones, Creed F. IIIen
dc.contributor.committeememberJia, Ruoxien
dc.contributor.committeememberLourentzou, Isminien
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.abstractDespite 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.en
dc.description.abstractgeneralWith the technologies we have today, people are constantly generating data. In this pool of information, gaining insight into the data proves to be extremely valuable. Deep learning is one method that allows for automatic pattern recognition by iteratively improving the disparity between its prediction and the ground truth. Complex models can learn complex patterns, and such models introduce challenges. This thesis explores the potential benefits of deep neural networks whether they stand to gain improvement despite the challenges. The models will be trained to segment intracranial aneurysms from volumetric images.en
dc.description.degreeMaster of Scienceen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectConvolutional Neural Networksen
dc.subjectIntracranial Aneurysmsen
dc.subjectImage Segmentationen
dc.titleDeep Convolutional Neural Networks for Segmenting Unruptured Intracranial Aneurysms from 3D TOF-MRA Imagesen
dc.typeThesisen Engineeringen Polytechnic Institute and State Universityen of Scienceen


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