Abstract
Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.
Highlights
The analysis of behavior in individual cells is essential to understand cellular processes subject to large cell-to-cell variations
Bulk measurements and cell synchronization methods are insufficient to study such processes because a lack of synchrony masks oscillations, all-or-none effects, sharp transitions, and other dynamic processes operating within individual cells [1,2,3,4,5,6,7,8]
High quality data for studying dynamic processes can only be obtained if segmentation is coupled with the ability to track cells, i.e., to correctly identify the same cell over consecutive time points in an experiment
Summary
The analysis of behavior in individual cells is essential to understand cellular processes subject to large cell-to-cell variations. Segmentation of individual cells relies on the ability to detect cell boundaries and classify all pixels in a given image as ‘cell’ or ‘non-cell’ pixels. This differentiation is accomplished by specifying a threshold or a threshold function. There are several ways in which this threshold can be determined, ranging from simpler intensity based thresholds to usage of more complex functions such as graphical models [10,11], pattern recognition [12], deformable templates [13], cell contours [14] or the watershed algorithm [15] Despite these efforts, we still lack a unified approach that robustly detects all cells for all time points
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