YeastNet is an artificial intelligence system developed by the National Research Council Canada in collaboration with the University of Ottawa that automatically segments and tracks yeast cells in microscope images for biomedical research applications. The system is currently in development as a research prototype that has been published in peer-reviewed literature. This technology is designed to improve the efficiency and accuracy of high-throughput biology experiments by automating the analysis of live-cell images, which would otherwise require manual processing.
YeastNet does not involve the collection or processing of personal information. The system is used by both government employees and members of the public research community who conduct biomedical experiments. Users interact with the system by providing microscope images of yeast cells, and the AI automatically identifies and tracks individual cells within those images, extracting quantitative data for downstream analysis.
The system uses deep learning algorithms trained on microscope images to recognize yeast cell structures and boundaries. Researchers can use YeastNet to accelerate their experimental workflows, reduce manual annotation time, and generate consistent, reproducible measurements of cellular behavior in biomedical research applications.
