JANUARY 30, 2009--Cell viability is one of the basic properties indicating the physiological state of the cell, and thus has long been one of the major considerations in biotech applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable but some of them might be invasive and even toxic to the target cells.
In support of automated noninvasive assessment of cell viability, a machine-vision system has been developed by researchers at Bielefeld University (Bielefeld, Germany; www.uni-bielefeld.de) based on supervised learning technique that learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via darkfield microscopy. For more information, go to: http://www.biomedcentral.com/content/pdf/1471-2105-9-449.pdf
