Abstracts

Cameraless Image Flow Cytometry and Image-Activated Cell Sorting Using Artificial Intelligence

by Rui Tang

High-throughput cellular image processing and analysis based on imaging flow cytometry (IFC) technology can bring significant insight to biology and medicine. The ability to classify, map and isolate cells based on high-content cellular images provides a powerful tool for biological and biomedical researchers and doctors to understand the connection between the phototype and genotype among the heterogeneous cell populations. This dissertation details the approach to conducting high-throughput cellular image analysis and low-latency real-time image processing using the IFC and artificial intelligence. As a result, we demonstrated the workflow for conducting high-throughput label-free cell studies on IFC systems and developed low-latency image-activated activated sorting (IACS) system using artificial intelligence and machine vision, opening a new venue for high-throughput cellular analysis and cell sorting based on machine vision and artificial intelligence.