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Differentiation of normal and cancerous urothelial cells from microscopic images using machine learning

Anže Mikec (2016) Differentiation of normal and cancerous urothelial cells from microscopic images using machine learning. MSc thesis.

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    As a result of a lack of reliable tooling, much of the cell detection in microscopic imaging is still done manually. This in turn raises research and treatment costs. To tackle this problem, we developed a tool, which automatically detects and classifies normal and cancerous urothelial cells. In the first part the tool segments microscopic images and marks the discovered cell regions. On the basis of the discovered regions, the tool extracts a set of features, which are later used for learning classification models. Neural nets, random forests, naive Bayes classificator, decision rules, SVM, boosting and bagging were used for classification. We used both automatically and manually marked images of normal pig cells and cancerous human cells. Empirical observation shows, that the tool segments cells really well, nonetheless, we noticed that classificators perform better on manually marked cells.The best results were achieved (using manually marked cells) by neural nets (AUC (area under the curve) 0,9052), bagging (AUC 0,9041) and random forests (AUC 0,9005). The performance of the tool was further tested with cytopathological urine samples. The results of image segmentation with these samples were noticeably worse than with other image sets. With future enhancements this tool could considerably contribute to simpler and more reliable microscopic image analysis of cancerous cells.

    Item Type: Thesis (MSc thesis)
    Keywords: cancerous cells, urothelial cancer, cancer cell classification, image segmentation, microscope image processing, microscope image segmentation, machine learning, cell morphology
    Number of Pages: 68
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Janez Demšar257Mentor
    izr. prof. dr. Mateja Erdani KreftComentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537279171)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3647
    Date Deposited: 18 Oct 2016 11:56
    Last Modified: 15 Nov 2016 08:19
    URI: http://eprints.fri.uni-lj.si/id/eprint/3647

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