Quantitative Analysis of Human Brain Tumour Cell Migration with Machine-Learning based 3D Computational Models

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dc.contributor.advisor Shim, Vickie
dc.contributor.advisor Park, Thomas
dc.contributor.advisor Wang, Alan
dc.contributor.author Vong, Chun Kiet
dc.date.accessioned 2021-09-29T20:05:14Z
dc.date.available 2021-09-29T20:05:14Z
dc.date.issued 2021 en
dc.identifier.uri https://hdl.handle.net/2292/56698
dc.description.abstract Cell migration plays a key role in many aspects of life, such as wound repair, embryonic development, brain plasticity and even in diseases like cancer. Thus, many experiments have sought to investigate the intricacies of cell migration, and a range of in vitro cell migration assays were developed to accommodate such experiments. The most common assay to date is the 2D monolayer wound healing assay, with more recent advancements in 3D cell cultures such as the spheroid assay, which better mimics the in vivo conditions and provides more physiologically relevant results. Although these assays have been integral to cell migration experiments, when analysing the results, only the overall migration has been quantified despite there is clear potential for additional analysis to the assays, particularly the regional migration analysis. This could provide more insights in migratory patterns and allow for a robust assessment of drug treatments in a chemotactic fashion, and finally provide more avenues of experimentation in 2D and 3D assays. To this extent, the aim of this thesis is to develop a pipeline that can quantify regional migration in spheroid assays and other assays such as the wound healing assay. This study developed a pipeline that automatically segments migrating edges of cells captured raw images from spheroid and wound healing assays via a machine learning approach. Thereafter, the finite element method was utilised to quantify both overall and regional migration in the segmented images. As a result, the pipeline was able to faithfully represent the treatment trends seen in the spheroid assay experiments, and it faithfully capture migration patterns in the phantom dataset and the spheroids. Additionally, the pipeline was generalized enough to be able to analyse wound healing experiments, albeit with some changes to the pipeline. This will be a basis for developing a machine-learning based computational tool for quantitative analysis of cell migration.
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof Masters Thesis - University of Auckland en
dc.relation.isreferencedby UoA en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Quantitative Analysis of Human Brain Tumour Cell Migration with Machine-Learning based 3D Computational Models
dc.type Thesis en
thesis.degree.discipline Bioengineering
thesis.degree.grantor The University of Auckland en
thesis.degree.level Masters en
dc.date.updated 2021-08-03T03:38:50Z
dc.rights.holder Copyright: the author en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en

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