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dc.contributor.advisorMiesfeld, Roger L.en
dc.contributor.authorHillis, Yingli
dc.creatorHillis, Yinglien
dc.date.accessioned2018-02-23T16:08:18Z
dc.date.available2018-02-23T16:08:18Z
dc.date.issued2017-12
dc.identifier.urihttp://hdl.handle.net/10150/626739
dc.description.abstractSphere-forming assay is an in-vitro technique to assess the self-renewal and differentiation potential of a homogenous or heterogenous population of cells. This technique is commonly used in the stem cell and cancer biology fields to assess the ability of a cell that is capable of self-proliferation and differentiation. (Schmitt, 2011, Lombaert et al., 2008) To detect proliferative growth, Ki-67, a marker of proliferation, is used in immunofluorescence staining of sphere-forming cells. The current gold standard methodology to quantify cell proliferation is to manually count the cells on images obtained using confocal microscopy. However, the reproducibility, the inter- and intra-subject variability, and the time requirement for manually counting cells are often major challenges for researchers. In this study, we propose a semi-automated cell segmentation algorithm using the FARSIGHT toolbox, to automatically count the individual three-dimensional (3-D) cell nuclei. The present work focused on the investigation of two aspects of the algorithm performance: sensitivity and specificity. We grouped images by sphere size to test specificity of the algorithm. For the sensitivity analysis, we tested the segmentation algorithm on both raw uncalibrated images and calibrated images using Fiji ImageJ software. We found that the proposed algorithm could efficiently identify cells and cell boundaries to overcome the background noise. Finally, statistical analysis showed the differentiation index had low percentage matching between the proposed method and the manual counting method.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.titleValidation of a Semi-Automatic Cell Segmentation Method to the Manual Cell Counting Method on Identifying Proliferating Cells in 3-D Confocal Microscope Imagesen_US
dc.typetexten
dc.typeElectronic Thesisen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.levelbachelorsen
thesis.degree.disciplineHonors Collegeen
thesis.degree.disciplineBiochemistryen
thesis.degree.disciplineMolecular and Cellular Biologyen
thesis.degree.nameB.S.en
refterms.dateFOA2018-08-14T22:42:13Z
html.description.abstractSphere-forming assay is an in-vitro technique to assess the self-renewal and differentiation potential of a homogenous or heterogenous population of cells. This technique is commonly used in the stem cell and cancer biology fields to assess the ability of a cell that is capable of self-proliferation and differentiation. (Schmitt, 2011, Lombaert et al., 2008) To detect proliferative growth, Ki-67, a marker of proliferation, is used in immunofluorescence staining of sphere-forming cells. The current gold standard methodology to quantify cell proliferation is to manually count the cells on images obtained using confocal microscopy. However, the reproducibility, the inter- and intra-subject variability, and the time requirement for manually counting cells are often major challenges for researchers. In this study, we propose a semi-automated cell segmentation algorithm using the FARSIGHT toolbox, to automatically count the individual three-dimensional (3-D) cell nuclei. The present work focused on the investigation of two aspects of the algorithm performance: sensitivity and specificity. We grouped images by sphere size to test specificity of the algorithm. For the sensitivity analysis, we tested the segmentation algorithm on both raw uncalibrated images and calibrated images using Fiji ImageJ software. We found that the proposed algorithm could efficiently identify cells and cell boundaries to overcome the background noise. Finally, statistical analysis showed the differentiation index had low percentage matching between the proposed method and the manual counting method.


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