Image Cytometry Analysis Using AI Techniques

Imaging Cytometry represents a significant advancement in modern scientific research, merging the swift analytical power of cytometry with the detailed cellular insights provided by cutting−edge imaging technologies. Imaging Cytometry has evolved remarkably from its initial concept in late 1970s. This evolution overcame initial obstacles, such as developing sophisticated data acquisition and analysis software. In our session, we will explore the forefront of initial applications, showcasing a variety of advanced techniques from high-throughput, label-free cell imaging and multi−parametric analysis for precise protein identification. To enhance image quality and reduce artifacts for a better data exploration, advanced denoising methods have been developed utilizing advanced deep-learning image processing algorithms. Furthermore, the incorporation of artificial intelligence (AI) into Imaging Cytometry has significantly improved our ability to analyze cellular morphology, opening new paths in cell biology and disease research. Our discussion will also cover the challenges in Imaging Cytometry implementation and the intricacies of AI integration for data analysis, and the ongoing quest for improvements in sensitivity and specificity. The session aims to offer an in−depth look at Imaging Cytometry’s current capabilities, the essential tools for its application, the obstacles to be overcome, and the future enhancements that could further empower this technology in scientific research.

CMLE Credit: 1.0