Right Ventricle Segmentation Using Cardiac Magnetic Resonance Images

Persistent Link:
http://hdl.handle.net/10150/612450
Title:
Right Ventricle Segmentation Using Cardiac Magnetic Resonance Images
Author:
Rosado-Toro, Jose A.
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Release after 25-May-2017
Abstract:
The world health organization has identified cardiovascular disease as the leading cause of non-accidental deaths in the world. The heart is identified as diseased when it is not operating at peak efficiency. Early diagnosis of heart disease can impact treatment and improve a patient's outcome. An early sign of a diseased heart is a reduction in its pumping ability, which can be measured by performing functional evaluations. These are typically focused on the ability of the ventricles to pump blood to the lungs (right ventricle) or to the rest of the body (left ventricle). Non-invasive imaging modalities such as cardiac magnetic resonance have allowed the use of quantitative methods for ventricular functional evaluation. The evaluation still requires the tracing of the ventricles in the end-diastolic and end-systolic phases. Even though manual tracing is still considered the gold standard, it is prone to intra- and inter-observer variability and is time consuming. Therefore, substantial research work has been focused on the development of semi- and fully automated ventricle segmentation algorithms. In 2009 a medical imaging conference issued a challenge for short-axis left ventricle segmentation. A semi-automated technique using polar dynamic programming generated results that were within human variability. This is because a path in a polar coordinate system yields a circular object in the Cartesian grid and the left ventricle can be approximated as a circular object. In 2012 there was a right ventricle segmentation challenge, but no polar dynamic programming algorithms were proposed. One reason may be that polar dynamic programming can only segment circular shapes. To use polar dynamic programming for the segmentation of the right ventricle we first expanded the capability of the technique to segment non-circular shapes. We apply this new polar dynamic programming in a framework that uses user-selected landmarks to segment the right ventricle in the four chamber view. We also explore the use of four chamber right ventricular segmentation to segment short-axis views of the right ventricle.
Type:
text; Electronic Dissertation
Keywords:
Polar Dynamic Programming; Polar Variance; Right Ventricle; Segmentation; Short Axis; Electrical & Computer Engineering; Four Chamber
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Rodriguez, Jeffrey J.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleRight Ventricle Segmentation Using Cardiac Magnetic Resonance Imagesen_US
dc.creatorRosado-Toro, Jose A.en
dc.contributor.authorRosado-Toro, Jose A.en
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.releaseRelease after 25-May-2017en
dc.description.abstractThe world health organization has identified cardiovascular disease as the leading cause of non-accidental deaths in the world. The heart is identified as diseased when it is not operating at peak efficiency. Early diagnosis of heart disease can impact treatment and improve a patient's outcome. An early sign of a diseased heart is a reduction in its pumping ability, which can be measured by performing functional evaluations. These are typically focused on the ability of the ventricles to pump blood to the lungs (right ventricle) or to the rest of the body (left ventricle). Non-invasive imaging modalities such as cardiac magnetic resonance have allowed the use of quantitative methods for ventricular functional evaluation. The evaluation still requires the tracing of the ventricles in the end-diastolic and end-systolic phases. Even though manual tracing is still considered the gold standard, it is prone to intra- and inter-observer variability and is time consuming. Therefore, substantial research work has been focused on the development of semi- and fully automated ventricle segmentation algorithms. In 2009 a medical imaging conference issued a challenge for short-axis left ventricle segmentation. A semi-automated technique using polar dynamic programming generated results that were within human variability. This is because a path in a polar coordinate system yields a circular object in the Cartesian grid and the left ventricle can be approximated as a circular object. In 2012 there was a right ventricle segmentation challenge, but no polar dynamic programming algorithms were proposed. One reason may be that polar dynamic programming can only segment circular shapes. To use polar dynamic programming for the segmentation of the right ventricle we first expanded the capability of the technique to segment non-circular shapes. We apply this new polar dynamic programming in a framework that uses user-selected landmarks to segment the right ventricle in the four chamber view. We also explore the use of four chamber right ventricular segmentation to segment short-axis views of the right ventricle.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectPolar Dynamic Programmingen
dc.subjectPolar Varianceen
dc.subjectRight Ventricleen
dc.subjectSegmentationen
dc.subjectShort Axisen
dc.subjectElectrical & Computer Engineeringen
dc.subjectFour Chamberen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineElectrical & Computer Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorRodriguez, Jeffrey J.en
dc.contributor.committeememberAltbach, Maria I.en
dc.contributor.committeememberBilgin, Alien
dc.contributor.committeememberMarefat, Michael M.en
dc.contributor.committeememberRodriguez, Jeffrey J.en
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