Heart Disease is a major cause of death in the developed world. The term heart disease refers to any abnormality of the cardiovascular system: this can include stroke, heart failure, coronary artery disease and heart attack. A key aspect of treating the disease is a clear image of what is happening within the heart. This is achieved using a variety of imaging modalities such as CT, ultrasound and MRI. While these modalities each have their pros and cons, MRI has been shown to be a reliable way to identify and diagnose issues related to heart health. The advantages of MRI include no radiation or ionizing contrast exposure, while still providing a clear image of the heart and other organs over time. Thanks to advances in computer vision there has been a recent effort to develop segmentation algorithms to quantify the various tissue types and how they change over time. While the primary focus of these efforts have targeted the muscle of the heart, recent research has called new attention to the fat that surrounds the heart and its link to coronary heart disease. This fat has proven difficult to examine in a practical way. The structure of the fat is irregular and follows an unclear trend for how it is distributed around the heart muscle. The work presented here attempts to segment the cardiac fat using deep learning. More specifically, both a traditional Neural Network (NN) and a Fully Convolutional Network (FCN) were investigated. FCNs are a type of artificial intelligence (AI) that uses convolution to predict the location of an object within an image. The use of this AI is a newer development that has been shown to be valuable in object identification. This particularly applies in cases where the application requires a mask to extract the feature vector for classification. FCNs can produce the mask to then be used to generate the feature vector which then gives the necessary information for classification to occur through a variety of methods.
|Advisor:||Klingensmith, Jon D.|
|Commitee:||Umbaugh, Scott, Wang, Yadong|
|School:||Southern Illinois University at Edwardsville|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Illinois|
|Source:||MAI 81/12(E), Masters Abstracts International|
|Subjects:||Medical imaging, Artificial intelligence|
|Keywords:||Artificial intelligence, Cardiac imaging, Magnetic resonance imaging, Medical imaging|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be