In today’s automation of procedures in microbiology, laboratory technicians still struggle to make the process of colony counting easier. They want to achieve more confidence in the accuracy of their total colony count and Colony Forming Unit (CFU) calculations.
Due to app colony counters being surprisingly limited, and professional lab equipment often outdated, automation of highly accurate colony counting has so far been out of reach.
In this drinking water use case, the company wanted to automate bacteria colony counting with high accuracy and high throughput, while distinguishing different kinds of bacteria. The challenges;
AI provides a breakthrough. You will learn how;
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Connect your laboratory devices, microscopes or quality inspection cameras to AI software, or directly upload plate images to start analyzing and counting colonies.
Powered by deep learning, the system starts analyzing images of bacteria colonies. The software gives initial predictions based on a powerful counting algorithm, encircling every single colony.
The predictions of colonies are presented to a microbiologist-in-the-loop to either correct or approve them. It learns quickly through real-time user feedback.
The more annotations and validations of the images are provided, the more advanced the model becomes. The predictions improve through interactive learning until deployment in real-time.
Understand how to obtain reproducable inspection results, taking errors out of manual and conventional counting systems.
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