Computing and Information Systems - Theses
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ItemAutomated analysis of time lapse microscopy images( 2012)Cells are the building blocks of life, and time lapse microscopy is a powerful way to study cells. Automated video acquisition and analysis of cells opens unprecedented opportunities, ranging from building novel mathematical models supported by rich data to automated drug screening. Unfortunately, accurate and completely automated analysis of cell images is a difficult task. Therefore human intervention is often required, for example, for tuning of segmentation and tracking algorithms or correcting the results of automated analysis. In this thesis, we aim to reduce the amount of manual work required, while preserving the accuracy of analysis. Two key tasks in automated analysis are cell segmentation and tracking. Segmentation is the process of locating cell outlines in cell images, while tracking refers to establishing cell identities across subsequent video frames. One of the main challenges of automated analysis is the substantial variability in cell appearance and dynamics across different videos and even within a single video. For example, there can be a few rapidly moving cells in the beginning of a video and a large number of cells stuck in a clump by the end of the video. Such variation has resulted in a large variety of cell segmentation and tracking algorithms. There has been a large body of work on automated cell segmentation and tracking. However, many methods make specific assumptions about cell morphology or dynamics, or involve a number of parameters that a user needs to set manually. This hampers the applicability of such methods across different videos. We first develop portable cell semi-segmentation and segmentation algorithms, where portability is achieved by using a flexible cell descriptor function. We then develop a novel cell tracking algorithm that has only one parameter, and hence can be easily adopted to different videos. Furthermore, we present a parameter-free variation of the algorithm. Our evaluation on real cell videos demonstrates that our algorithms are capable of achieving accurate results and outperforming other existing methods. Even the most sophisticated cell tracking algorithms make errors. A user can be required to manually review the tracking results and correct errors. To this end, we propose a semi-automated tracking framework that is capable of identifying video frames that are likely to contain errors. The user can then look only into these frames and not into all video frames. We find that our framework can significantly reduce the amount of manual work required to review and correct tracking results. Furthermore, in different videos, the most accurate results can be obtained by different methods and different parameter settings. It is often not clear which method should be chosen for a particular video. We address this problem with a novel method for ranking cell tracking systems without manual validation. Our method is capable of ranking cell trackers according to their fitness to a particular video, without the need for manual collection of the ground truth tracks. We simulate practical tracking scenarios and confirm the feasibility of our method. Finally, as an example of a biological assay, we consider evaluating the locomotion of Plasmodium parasites (that cause malaria) with application to automated anti-malaria drug screening. We track live parasites in a matrigel medium and develop a numerical description of parasite tracks. Our experiments show that this description captures changes in the locomotion in response to treatment with the toxin Cytochalasin D. Therefore our description can form a basis for automated drug screening, where various treatments are applied to different cell populations by a robot, and the resulting tracks are evaluated quantitatively. In summary, our thesis makes six major contributions highlighted above. These contributions can reduce the amount of manual work in cell image analysis, while achieving highly accurate results.