Development of a motion-based cell-counting system for Trypanosoma parasite using a pattern recognition approach

Abstract

Automated cell counters that utilize still images of sample cells are widely used. However, they are not well suited to counting slender, aggregate-prone microorganisms such as Trypanosoma cruzi. Here, we developed a motion-based cell-counting system, using an image-recognition method based on a cubic higher-order local auto-correlation feature. The software successfully estimated the cell density of dispersed, aggregated, as well as fluorescent parasites by motion pattern recognition. Loss of parasites activeness due to drug treatment could also be detected as a reduction in apparent cell count, which potentially increases the sensitivity of drug screening assays. Moreover, the motion-based approach enabled estimation of the number of parasites in a co-culture with host mammalian cells, by disregarding the presence of the host cells as a static background. METHOD SUMMARY Cubic higher-order local auto-correlation is a method to extract features related to shape and movement of moving objects. This motion image recognition method can be applied to a cell-counting system for motile microorganisms, by learning the motion patterns observed in images of known cell densities as a training dataset to estimate the cell densities of unknown samples.