Spatio-temporal cell segmentation and tracking for automated screening

A growing number of screening applications require the automated monitoring of cell populations including cell segmentation, tracking, and measurement. We present general methods for cell segmentation and tracking that exploit the spatio-temporal nature of the task to constrain segmentation. The images are de-noised and segmented by combining wavelet coefficients at various levels, thus enabling extraction of cells in images with low contrast-to-noise ratios. Each track of clustered cells resulting from association of nearby cells in the spatio-temporal volume is then split into individual cells by evolving sets of contours from other slices. The hypothesis whether to split or merge objects making up the cluster is tested using learned features trained from single track cells. Due to the difficult nature of generating ground truth, we also present a framework for edit-based validation whereby the user corrects the edits made by the automatic system rather than generating the truth from scratch. The results show the promise of the approach and demonstrate the ability of the algorithms to provide meaningful measurements of cell response to drug treatment in low-dose Hoechst-stained cells.