http://repub.eur.nl/res/aut/17864/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/34951/ 2012-01-23T00:00:00Z Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images. http://repub.eur.nl/res/pub/22965/ 2011-04-13T00:00:00Z Biological research is impossible to imagine without a microscope. Latest genera- tions of microscopes, able to produce huge arrays of multidimensional data, only distantly resemble Leeuwenhoek’s first microscope. Every advance in visualization techniques and hardware brings us one step closer to understanding life, e.g., how genome information gives identity to cells, how cells constitute organisms and how errant cells cause disease. Discovery of the green fluorescent protein (GFP) in the nineties of the previous century was definitely one of the most impor- tant milestones on that path, giving new strong impulse to the field of fluorescence microscopy. http://repub.eur.nl/res/pub/28411/ 2010-08-11T00:00:00Z Motivation: Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. Results: Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. http://repub.eur.nl/res/pub/17041/ 2009-10-01T00:00:00Z The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology. http://repub.eur.nl/res/pub/15155/ 2008-09-10T00:00:00Z Segmentation and tracking of cells in fluorescence microscopy image sequences is an important task in many biological studies into cell migration as well as intracellular dynamics. The growing size and complexity of biological image data sets precludes manual analysis, and calls for increasingly advanced automatic algorithms that are generic enough to be easily tunable to different applications, yet robust enough to deal with different cell types and strongly varying imaging conditions. Active-contour based algorithms have proven to be very suitable for this purpose but still suffer from several short-comings that limit their segmentation accuracy and tracking robustness. In addition, these algorithms are generally rather computationally expensive. In this paper, we present an advanced level-set based multiple-cell segmentation and tracking algorithm, which implements seven modifications compared to earlier algorithms that considerably improve its performance. Preliminary experiments on three different time-lapse fluorescence microscopy images demonstrate the potential of the new algorithm.