Model-based automated detection of mammalian cell colonies

Manually counting cell colonies, especially those that originate from fibroblast cell lines, is a time-consuming, eye-straining and tedious task in which consistency of counting is difficult to maintain. In this paper we present a novel model-based image segmentation method, which employs prior knowledge about the shape of a colony with the aim to automatically detect isolated, touching and overlapping cell colonies of various sizes and intensities. First, a set of hypothetical model instances is generated by using a robust statistical approach to estimate the model parameters and a novel confidence measure to quantify the difference between a model instance and the underlying image. Second, the model instances matching the individual colonies in the image are selected from the set by a minimum description length principle. The procedure was applied to images of Chinese hamster lung fibroblast cell line DC3F, which forms poorly defined or 'fuzzy' colonies. The correlation with manual counting was determined and the cell survival curves obtained by automated and manual counting were compared. The results obtained show that the proposed automatic procedure was capable to correctly identify 91% of cell colonies typical of mammalian cell lines.