Automated analysis of time lapse microscopy images

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