Abstract
In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.
Highlights
Mathematical image analysis techniques have recently become enormously important in biomedical research, which increasingly needs to rely on information obtained from images
In order to evaluate the effects of a given drug on mitosis, it is desirable to measure average mitosis durations and distribution of possible outcomes such as regular division into two daughter cells, apoptosis, division into an abnormal number of daughter cells and no division at all [4,5]
In the following we present our proposed workflow designed in order to facilitate mitosis analysis in live-cell phase contrast imaging experiments
Summary
Mathematical image analysis techniques have recently become enormously important in biomedical research, which increasingly needs to rely on information obtained from images. Observation of cell cultures in live-cell imaging experiments by means of sophisticated light microscopy is a key technique for quality assessment of anti-cancer drugs [2,3]. In this context, analysis of the mitotic phase plays a crucial role. The balance between mitosis and apoptosis is normally carefully regulated, but many types of cancerous cells have evolved to allow. Drugs targeting mitosis are used extensively during cancer chemotherapy. In order to evaluate the effects of a given drug on mitosis, it is desirable to measure average mitosis durations and distribution of possible outcomes such as regular division into two daughter cells, apoptosis, division into an abnormal number of daughter cells (one or P 3) and no division at all [4,5]
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