Reagent-free automatic cell viability determination using neural networks based machine vision and dark-field microscopy in Saccharomyces cerevisiae.

Fermentation industries require in-situ real-time monitoring of cell viability during fermentation processes. For this purpose, reagent-free approaches are desired because they can be used for in situ analysis and reduce the system's complexity. We have developed an automatic way of determining cell viability via analysis of time-lapse image sequences taken by dark field microscopy without the aid of any additional reagents. The image processing is based on neural networks based machine vision, involving Principal Component Analysis (PCA) to investigate the dynamic information of intracellular movements. In consequence, the essential features as the vital sign of the target cells are discovered. Viability predictions using the Support Vector Machine (SVM) classifier have been done successfully on the datasets with different qualities. Accuracy up to above 90% has been obtained on the basis of image enhancement. Robustness of the system is proved by the results of the tests. The model organism we have used is Saccharomyces cerevisiae, however, this technique can promisingly be applied for the identification of cell viability of other organisms as well.