Reconstructing Organ Vasculature and Identifying Morphological Changes through the Utilization of Sequential Block-Face Imaging by Milling with Ultraviolet Excitation

dc.contributor.advisorMohan, Chandra
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberGifford, Howard C.
dc.creatorAbunofal, Omar
dc.date.accessioned2022-09-08T15:03:22Z
dc.date.createdDecember 2021
dc.date.issued2021-12
dc.date.updated2022-09-08T15:03:23Z
dc.description.abstractMedical imaging is a significant medical procedure for diagnosing and characterizing various diseases. The gold standard in diagnosing diseases using medical imaging is imaging a stained tissue biopsy using standard staining procedures under light microscopy. Besides that, nuclear imaging modalities are commonly used for volumetric imaging, especially for internal organs. As a multitude of imaging modalities are emerging, various limitations within different imaging modalities remain existent. 2-dimensional (2D) imaging displays limited information to the pathologists due to the limited imaging depth. 3-dimensional (3D) imaging, namely nuclear imaging, presents several challenges in the medical field due to the minimal accessibility in medical facilities, expensive procurement costs, and challenges with data interpretation. In this work, we propose a novel imaging system that allows 3D imaging from 2D scans through a facile approach. The imaging system is based on the use of a motorized microtome for sectioning paraffin-embedded biological samples along with the use of UV light for stain excitation. Thus, we employ serial block-face imaging of paraffin-embedded biological samples from wild-type and systemic lupus erythematosus (SLE) mice that were intracardially perfused with India-ink for vascular staining. We were interested in reconstructing the vasculature of the mouse liver and creating 3D rendering models of the kidney glomeruli in order to observe the extent of SLE manifestation that our imaging system can aid in identifying. We designed a mold chamber that can be used for deep paraffin embedding since we aim to accomplish volumetric imaging of 3 mm to 1 cm depth. Following image stacking, we performed image alignment and vascular segmentation for the liver as part of the image processing procedure. We conclude that our imaging system is a robust platform yet a simple and inexpensive modality for 3D imaging. More research is warranted to transfer our work and findings from qualitative to quantitative models for assessing various disease characteristics within biological organs. Moreover, automating the system to achieve user-friendliness and high throughput would be a groundbreaking achievement that can lead the way to the commercialization of the imaging system.
dc.description.departmentBiomedical Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/11009
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. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMUVE
dc.subject3D Imaging
dc.subjectSLE
dc.subjectSegmentation
dc.titleReconstructing Organ Vasculature and Identifying Morphological Changes through the Utilization of Sequential Block-Face Imaging by Milling with Ultraviolet Excitation
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2023-12-01
local.embargo.terms2023-12-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentBiomedical Engineering, Department of
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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