Speed up counting,
maintain accuracy

The Corning Cell Counter generates images that are automatically analyzed using our state-of-the-art cell counting algorithm. Images are analyzed within seconds and can be used to count both mammalian cells and organoids. This allows you to quickly obtain an accurate overview of the cell concentration of your sample. Users can optimize their cell counts by gating the size range to the cell type that is being used. The gates and corresponding concentration, cell size and viability analysis can be adapted in real time.

To identify single cells using image analysis is relatively straightforward. Cell recognition can be achieved using small parameter sets that contain features like cell size, edge thickness and circularity. Some cell types however are known to stick together and as a consequence would be difficult to single out. The image analysis algorithms in the Corning Cell Counter are supported by machine learning where the parameter set has been extended to three-hundred and fifty thousand and is optimized to clearly distinguish cell boundaries, even in samples that are highly crowded.

The popularity of organoid culturing has spiked in the past decade since the publication of Sato et al. 2009, in which the development of intestinal crypt-villus structures is postulated. The three-dimensional niche-like structures within organoids are highly similar to in situ physiology. While these structures have a high potential in many life-science research fields, they are laborious to develop and maintain. The Corning Cell Counter can be equipped with an algorithm for organoid counting that can be used to standardize and speed-up a routine part of organoid culturing.