Model-based automated detection of mammalian cell colonies

Manually counting cell colonies, especially those that originate from fibro blast cell lines, is a time-consuming, eye-straining and tedious task in wh ich consistency of counting is difficult to maintain. In this paper we pres ent a novel model-based image segmentation method, which employs prior know ledge about the shape of a colony with the aim to automatically detect isol ated, touching and overlapping cell colonies of various sizes and intensiti es. First, a set of hypothetical model instances is generated by using a ro bust statistical approach to estimate the model parameters and a novel conf idence measure to quantify the difference between a model instance and the underlying image. Second, the model instances matching the individual colon ies in the image are selected from the set by a minimum description length principle. The procedure was applied to images of Chinese hamster lung fibr oblast cell line DC3F, which forms poorly defined or 'fuzzy' colonies. The correlation with manual counting was determined and the cell survival curve s obtained by automated and manual counting were compared. The results obta ined show that the proposed automatic procedure was capable to correctly id entify 91% of cell colonies typical of mammalian cell lines.