3D-2D Representation Learning for Longitudinal Analysis Using Single-Slice CT Scans

Simple item page
dc.contributor.advisorLandman, Bennett A.
dc.contributor.committeeChairLandman, Bennett A.
dc.creatorYu, Xin
dc.creator.orcid0000-0002-3388-9606
dc.date.accessioned2024-08-16T10:08:29Z
dc.date.available2024-08-16T10:08:29Z
dc.date.created2024-08
dc.date.issued2024-07-16
dc.date.submittedAugust 2024
dc.date.updated2024-08-16T10:08:29Z
dc.description.abstractAbdominal computed tomography (CT) offers high-resolution tissue maps that facilitate the quantification of body composition. The existing longitudinal CT dataset from the Baltimore Longitudinal Study of Aging (BLSA) enables us to explore the relationship between body composition and cognitive decline. To minimize radiation exposure, 2D axial scans are used instead of 3D volumetric scans, presenting several challenges. Notably, 2D scans lack the contextual information inherent in 3D data, complicating body composition segmentation. Furthermore, difficulties in consistently positioning cross-sectional scans mean that 2D slices may not always be taken at the same vertebral level, resulting in variability in the abdominal slices obtained from patients over different years. In this dissertation, we utilize existing 3D volumetric data for representation learning to mitigate the anatomical limitations present in single-slice data. We apply advanced deep learning techniques for segmentation and generation to address these challenges. Ultimately, we identify a potential underlying phenotype associated with cognitive decline using the derived body composition metrics, providing new insights into the connection between the body and the brain.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1803/19262
dc.language.isoen
dc.subject3D-2D, Single-Slice CT, Body Composition
dc.title3D-2D Representation Learning for Longitudinal Analysis Using Single-Slice CT Scans
dc.typeThesis
dc.type.materialtext
thesis.degree.disciplineComputer Science
thesis.degree.grantorVanderbilt University Graduate School
thesis.degree.levelDoctoral
thesis.degree.namePhD
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
YU-DISSERTATION-2024.pdf
Size:
22.42 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.92 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.24 KB
Format:
Plain Text
Description:
Download
Collections
Electronic Theses and Dissertations