Adaptive Segmentation of Particles and Cells for Fluorescent Microscope Imaging

Analysis of biomolecules in cells essentially relies on fluorescence microscopy. In combination with fully automatic image analysis it allows for insights into biological processes on the sub-cellular level and thus provides valuable information for systems biology studies. In this paper we present two new techniques for automatic segmentation of cell areas and included sub-cellular particles. A new cascaded and intensity-adaptive segmentation scheme based on coupled active contours is used to segment cell areas. Structures on the sub-cellular level, i.e. stress granules and processing bodies, are detected applying a scale-adaptive wavelet-based detection technique. Combining these results yields fully automated analyses of biological processes, and allows for new insights into interactions between different cellular structures and their distributions among different cells. We present an experimental evaluation based on ground-truth data that confirms the high-quality of our segmentation results regarding these aims and opens perspectives towards deeper insights into biological systems for other problems from systems biology.