Dynamic morphology-based characterization of stem cells enabled by texture-based pattern recognition from phase-contrast images

The increased use of stem cells to study disease states in vitro has created a need for tools that provide automated, non-invasive, and objective characterization of cell cultures. In this work, we address this need by developing a novel framework for stem cell assessment using time-lapse phase-contrast microscopy and automated texture-based analysis of images. We capture and quantify morphological changes during stem cell colony growth by segmenting each image of the time-lapse sequence into five distinct classes of cells. We apply our automated classification to enable non-invasive estimation of cell doubling time, and demonstrate applications of the presented framework for quantitative assessment of cell culture conditions.