Grant: Project Grant

Cell analytics methodology is essential for medical and life science research to study diseases and develop new medicines. However, high instrumentation and reagent costs, labor-intensive manual methodologies, and lack of analytical integration, are major obstacles for performing high-quality research. Here, we propose to use computer vision, a subset of deep learning, and convolutional neural networks, to develop pattern recognition algorithms for a variety of cell assays and readouts, using images taken in a light transmission microscope. Generated software will help create a powerful integrated toolbox of diverse cell analytical methodology such as cell counting, clonogenic assay quantification, cell motility, and 3D culture assessment. The software will be compatible with iOS and Android, and allow researchers to get instant cell analytical read-outs by analyzing pictures of cells on mobile phones, tablets, or any other computer-based workstation. This project will deliver a commercial toolbox for cell analytics that is fast, accurate, and accessible to any life science laboratory worldwide. The product will be commercialized through our UBC spin-out company SnapCyte Solutions in collaboration with UBC.