Segmentation, Analysis and Visualization of Large Microvascular Networks in the Mouse Brain

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dc.contributor.advisorChen, Guoning
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberEriksen, Jason
dc.contributor.committeeMemberPandurangan, Gopal
dc.contributor.committeeMemberShah, Shishir Kirit
dc.creatorGovyadinov, Pavel A. 1989-
dc.creator.orcid0000-0001-7980-2791
dc.date.accessioned2019-09-15T22:03:22Z
dc.date.available2019-09-15T22:03:22Z
dc.date.createdAugust 2019
dc.date.issued2019-08
dc.date.submittedAugust 2019
dc.date.updated2019-09-15T22:03:23Z
dc.description.abstractAdvances in high-throughput microscopy allow researchers to collect three-dimensional images of whole organ vascular networks at sub-micrometer resolution. Microvascular networks are highly sparse and space-filling, making them difficult to segment, visualize, and analyze. Since microvasculature plays a prominent role in tissue function and development, overcoming these limitations could lead to advances in disease diagnosis and grading. There is therefore a compelling need for algorithms that segment, visualize, and analyze organ-scale microvascular networks at sub-cellular resolution. In this dissertation, I present a platform enabling scalable segmentation, visualization, and analysis of microvascular networks encoded within multi-terabyte three dimensional microscope images. I first propose a highly parallel and scalable algorithm to segment microvascular networks on GPUs, providing domain experts with an accessible tool usable on standard workstations. I then propose multiple visualization methods that enable the study of complex microvascular networks, highlighting important structural features that are challenging to evaluate with traditional volumetric and rasterization tools. Finally, I demonstrate that my platform can be extended to collect statistical features and identify metrics for tissue classification and characterization.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: P. A. Govyadinov, T. Womack, J. L. Eriksen, G. Chen, and D. Mayerich. Ro-bust tracing and visualization of heterogeneous microvascular networks.IEEEtransactions on visualization and computer graphics, 25(4):1760–1773, 2018. And in: J. Guo, K. A. Keller, P. Govyadinov, P. Ruchhoeft, J. H. Slater, and D. May-erich. Accurate flow in augmented networks (afan): an approach to generatingthree-dimensional biomimetic microfluidic networks with controlled flow.An-alytical methods, 11(1):8–16, 2019.
dc.identifier.urihttps://hdl.handle.net/10657/4702
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectSegmentation
dc.subjectVisualization
dc.subjectGraph
dc.subjectImage processing
dc.subjectMicrovasculature
dc.titleSegmentation, Analysis and Visualization of Large Microvascular Networks in the Mouse Brain
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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