Label-Free Quantification of Cell Growth and Morphology using Artificial Intelligence and Advanced Data Analytics Webinar

Live-cell imaging enables acquisition of phase contrast images and provides an ideal platform to study multifaceted biological paradigms in drug discovery. The movement of these models towards increasingly complex ones, using more relevant and precious cell types, has highlighted the importance of label-free analysis methods that are non-perturbing. Incorporating Artificial Intelligence (AI), and data-science based algorithms, into user-friendly workflows has enabled powerful quantification of a wide range of cellular models. 

We demonstrate an automated, robust solution for label-free cell segmentation using integrated AI-based software. The Incucyte^® AI Confluence analysis, driven by a pre-trained convolutional neural network (CNN), allows us to reliably monitor cell proliferation in a non-perturbing unbiased manner with minimal user input. In this webinar, we show validation of accurate segmentation across cell types with different morphologies. 

Analysis of cell morphology is a powerful technique that can provide insight into cell viability and behavior. The Incucyte^® Advanced Label-Free Classification Software Module uses sophisticated multivariate analysis to monitor two user-defined populations based on multiple aspects of cell shape. We demonstrate the application of this simplified workflow to biological models that undergo morphological changes, including a Live/Dead and differentiation assay. 

We also exemplify how AI Confluence or Advanced Label-fee Classification can provide high-throughput physiologically relevant insight into cell heath and be utilized for the investigation of compound efficacy in drug discovery.

Overall, live-cell imaging and intuitive label-free analysis is a powerful approach that provides objective, meaningful quantitative analysis of complex biological behavior.