Automated counting of mammalian cell colonies

Investigating the effect of low-dose radiation exposure on cells using assa ys of colony-forming ability requires large cell samples to maintain statis tical accuracy. Manually counting the resulting colonies is a laborious tas k in which consistent objectivity is hard to achieve. This is true especial ly with some mammalian cell lines which form poorly defined or 'fuzzy' colo nies, typified by glioma or fibroblast cell lines. A computer-vision-based automated colony counter is presented in this paper. It utilizes novel imag ing and image-processing methods involving a modified form of the Hough tra nsform. The automated counter is able to identify less-discrete cell coloni es typical of these cell lines. The results of automated colony counting ar e compared with those from four manual (human) colony counts for the cell l ines HT29, A172, U118 and IN1265. The results from the automated counts fal l well within the distribution of the manual counts for all four cell lines with respect to surviving fraction (SF) versus dose curves, SF values at 2 Gy (SF2) and total area under the SF curve (Dbar). From the variation in t he counts, it is shown that the automated counts are generally more consist ent than the manual counts.