Model-based approach for tracking embryogenesis in Caenorhabditis elegans fluorescence microscopy data

The nematode Caenorhabditis elegans (C. elegans) is a widely used model organism in biological investigations. Due to its well-known and invariant cell lineage tree, it can be used to study the effects of mutations and various disease processes. Effective and efficient analysis of the wealth of time-lapse fluorescence microscopy image data acquired in such studies requires automation of the cell segmentation and tracking tasks involved. This is hampered by many factors, including autofluorescence effects, low and uneven contrast throughout the images, high noise levels, large numbers of possibly simultaneous cell divisions, and touching or clustering cells. In this paper, we present a new algorithm for segmentation and tracking of cells in C. elegans embryogenesis image data. It is based on the model evolution framework for image segmentation and uses a novel multi-object tracking scheme based on energy minimization via graph cuts. Preliminary experiments on publicly available test data demonstrate the potential of the algorithm compared to existing approaches.