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The Importance of Cell Counting

Cell counting refers to the number of cells within a well-defined volume of a sample. The results from a cell counting process is often used to determine cell viability and behavior and testing efficacy of active compounds on cell lines.
Cell counting is also used by researchers to study the viability and behavior of infectious viruses, bacteria and different model organisms such as C. elegans and dictyostelium; by continuously monitoring cell counts of a sample, the conditions for cell growth and growth restrictions are identified, thus finding ways to eradicate infectious pathogens and identification of the best active compounds.
Furthermore, an accurate cell count calculates the number of dead and living cells in a cell culture, which determines the life expectancy of cancerous cells.

Manual Cell Counting Versus Automated Cell Counting.

The most popular method for cell counting is using a Hemocytometer, which is inexpensive but laborious and prone to user bias.
Counting cells with Hemocytometers requires a lot of training and entails long hours of performing a tedious and repetitive task, which can be detrimental to researchers’ physical well-being as this practice has the potential of causing great physical strains to the experienced researcher.
Automated cell counting instruments provide a solution to the tedious practice of manual cell counting, however the price associated with these devices are often too high for laboratories to acquire, leaving the important procedure of delivering accurate cell counts to manual practices that are time consuming, physically laborious and do not guarantee accuracy.

The Role of Machine Learning In Cell Counting

The Machine Learning technologies delivered by Rapid Biolabs provide a quick, cost-effective and accessible software solution to researchers and medical institutions.
Applying image processing practices, microscopy cell images are processed, classified and calculated, with transparent results that reveal the level of accuracy per sample.
The data used to train our models are acquired through conventions with educational institutions which ensures a high level of accuracy and guarantees fast results at accessible prices.