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Mathematical Image Analysis for Cancer Research

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Research in biomedical sciences is increasingly relying on digital images. At the same time, technical equipment for data acquisition and storage media are developing rapidly, raising an urgent need for suitable image enhancement and processing techniques. In mathematical imaging a vast variety of different models and methods exists to track cells with fluorescence markers. However, this technique has some disadvantages, especially the staining often causing cell death. Consequently, live-cell imaging, in particular observation of mitotic events, is very difficult or even impossible. In contrast to fluorescence microscopy, phase contrast microscopy yields many advantages and facilitates live-cell imaging experiments since staining is avoided. One specific problem we would like to address in this talk is the development of tools for automatic mitosis detection and tracking of cancer cells. Time-lapse observations of cell divisions are a measurement to determine the percentage of cells undergoing mitosis (mitotic index analysis). The mitotic index is an important prognostic factor predicting both overall survival and response to chemotherapy in most types of cancer. Durations of the cell cycle and mitosis vary in different cell types. An elevated mitotic index indicates more cells are dividing, and thus is one of the key measurements in cancer drug development studies.

This talk is part of the Computational and Systems Biology series.

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