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Computer Vision And Machine Learning Based Adaptable Conversıon Method For Any Light Microscope To Automated Cell Counter By Trypan Blue Dye-Exclusion

BROWSE_DETAIL_CREATION_DATE: 08-08-2017

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: EngD

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_CREATORS: Özkan, Akın (Author),

BROWSE_DETAIL_CONTRIBUTERS: İşgör, Sultan Belgin (Advisor),

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cell counting, cell viability, light microscope, hemocytometer, HL60, K562


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Almost all of the cell biology experiments involve counting of cells regularly to monitor cell proliferation and viability. Knowledge of the cell quantity and quality are important parameters for the experimental standardization and toxicity impact estimation. There are two different approaches to count the cells, such as, hemocytometer-based manual counting, and usage of an automated cell counter. Either of the methods have their advantages and disadvantages. High investment and operational cost limit the wide range usage of automated cell counters. On the other hand, manual cell counting based on hemocytometer has various limitations by the fact that reliability of cell counting highly depends on operator’s experience. Moreover, high estimation time requirement and human labor are two more drawbacks of the manual process. This thesis proposes state-of-the-art alternative method (i.e. framework) for the cell counting by defining computer vision and machine learning based conversion methodology. The basis of the proposed method is the adaptation of hemocytomer-based manual counting to automated procedure by adding middleware decision software to reduce its shortcomings. In addition, two novel data sets are collected to test our proposed method in terms of cell counting (i.e non-stained) and cell viability analysis (i.e. stained). The datasets are available for non-profit public usage from “biochem.atilim.edu.tr/datasets/” which will be baseline to future studies on this research domain. Both datasets contain two different types ofvcancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562). From our experimental results, our method reaches up to 92% and 74% in terms of recall scores for HL60 and K562 cancer cells, respectively, with the high precision. The experimental results also validate that the proposed method can be a powerful alternative to the current cell counting approaches.


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