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Machine learning based image segmentation for large-scale osteoarthritis analysis

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TitleInfo
Title
Machine learning based image segmentation for large-scale osteoarthritis analysis
Name (type = personal)
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Tan
NamePart (type = given)
Chaowei
NamePart (type = date)
1983-
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Chaowei Tan
Role
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author
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NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris N.
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Dimitris N. Metaxas
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Advisory Committee
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chair
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NamePart (type = family)
Li
NamePart (type = given)
Kang
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Kang Li
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Yu
NamePart (type = given)
Jingjin
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Jingjin Yu
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Advisory Committee
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RoleTerm (authority = RULIB)
internal member
Name (type = personal)
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Dou
NamePart (type = given)
Xin
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Xin Dou
Affiliation
Advisory Committee
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2020
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2020-01
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2020
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English
Abstract (type = abstract)
Osteoarthritis (OA) is the most common degenerative joint disease worldwide, tending to occur in the joints of hip and knee. Large adult population in the United States have been affected by OA, and by 2030, an estimated 20 percent of Americans (about 70 million people) may be at increased risk for this disease. Effective medical image segmentation methods play fundamental roles in the clinical analysis of the disease. In this dissertation, three machine learning based segmentation for knee cartilage, femoral head-neck junction and thigh muscular/adipose tissue are discussed, respectively. Furthermore, large-scale OA analysis on the knee and hip joints could be further implemented based on these segments.

Knee cartilages (i.e., femoral, tibial, and patellar cartilage) are essential tissue for knee radiographic OA diagnosis. Effective segmentation of knee cartilages in large-sized and high-resolution 3D magnetic resonance (MR) data is firstly proposed. The key contribution is an adversarial learning based collaborative multi-agent network. The method employs three parallel segmentation agents to label cartilages in their respective region of interest (ROI), and then fuses the three cartilages by a ROI-fusion layer and drive a collaborative learning by an adversarial sub-network. The ROI-fusion layer not only fuses the individual cartilages, but also backpropagates the training loss from the adversarial sub-network to each agent to enable joint learning of shape and spatial constraints. The proposed scheme is shown robust and accurate in knee cartilage segmentation, and it is effective for cartilage biomarkers (e.g., surface area, volume) estimation in large-scale quantitative tests. Second, a deep multi-task learning network is exploited for the shape-preserved segmentation of the proximal part of femur (i.e., femoral head and neck) in 2D MR images. This method combines the tasks of region identification and boundary distance regression, and thus enables the task-specific feature learning for continuous segmented object with smooth boundary. This bone joint depiction could help the measurements of the femoral head-neck morphology and reflect the evolution of hip OA. In the last part of the dissertation, the muscular and adipose tissue extraction in 3D MR thigh data is investigated by an integrated framework. Specifically, deformable models and learning based detection/classification are integrated into the framework to enable robust tissue quantification for a large-scale analysis of OA-related thigh tissue changes.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = local)
Topic
Osteoarthritis analysis
Subject (authority = LCSH)
Topic
Osteoarthritis -- Imaging
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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Identifier
ETD_10478
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application/pdf
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text/xml
Extent
1 online resource (xiv, 84 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-cwh6-ct31
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tan
GivenName
Chaowei
Role
Copyright Holder
RightsEvent
Type
Permission or license
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2019-12-22 23:20:09
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Name
Chaowei Tan
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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