Creation Of Algorithms For Quantitation Of Ex Vivo Organ Development Over Time

Author(s)Anderson, Alexis
Date Accessioned2022-07-06T14:32:44Z
Date Available2022-07-06T14:32:44Z
Publication Date2022-05
AbstractRegenerative medicine is a promising field that would help overcome frequent issues of donor supply shortages as well as immune complications in direct transplantations through the generation of functional tissues and organs as replacements instead. The key to this is to understand cell assembly and the mechanisms responsible for driving organ and tissue morphogenesis. In order to do this, one of the first steps is the 3D spatial identification of proliferating cells and quantitation of patterns and locations of growth in the developing kidney over time in culture. However, the most immediate barrier to being able to identify the proliferating sites is being able to effectively and efficiently segment the individual images in the z-stack from the background for reconstruction. Therefore, the goal of this thesis is to develop a variety of semi-automated algorithms to address the complications of segmentation (AIM 1) and then proliferation (AIM 2) for temporally acquired image stacks. Ultimately, the segmentation algorithm achieved a fast and reliable semi-automated method for segmentation in comparison to original hand segmentation, and the proliferation algorithm achieved an initial identification of all proliferating cells in a z-stack and an initial plot of all of them in 3D space. Although there are improvements that could be made, these algorithms are a great first step towards truly understanding the mechanisms for proliferation within the developing kidney and eventually cell assembly and regenerative medicine.en_US
AdvisorJason Gleghorn
ProgramBiomedical Engineering
URLhttps://udspace.udel.edu/handle/19716/31064
PublisherUniversity of Delawareen_US
KeywordsRegenerative medicineen_US
KeywordsCell assemblyen_US
KeywordsKidneyen_US
TitleCreation Of Algorithms For Quantitation Of Ex Vivo Organ Development Over Timeen_US
TypeThesisen_US
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