Label-Free Quantification of Cell Growth and Morphology Using AI and Advanced Data Analytics

Live-cell imaging enables acquisition of phase contrast images - providing an ideal platform to study multifaceted biological paradigms in drug discovery. The shift towards complex models, using more relevant and precious cell types, highlights the importance of label-free analysis methods that are non-perturbing.

Incorporating Artificial Intelligence (AI), and data-science based algorithms, into user-friendly workflows enables powerful quantification of a wide range of cellular models.

In this webinar we demonstrate an automated, robust solution for label-free cell segmentation using integrated AI-based software.

Explore:
 How an integrated AI-driven approach provides accurate measurements of proliferation
 Validation of label-free analysis methods for robustly quantifying cell health in a non-perturbing manner
 How live-cell imaging and intuitive label-free analysis can be built into your development workflow

00:14 We’ve developed AI driven, convolutional neuro network-based algorithms that measure cell confluence. This simplified label-free workflow, which is shown here, allows for the identification and segmentation of cells with increased accuracy and minimal user input. This provides us with real time measurements of cell proliferation and cell health.

00:40 Next, we exemplify how using cell by cell analysis and univariant metrics such as phased area we can quantify phagocytosis in a label-free manner.

00:51 Here we exemplify how both AI confluence and advanced label-free classification can be used to quantify cell health.

Be sure to subscribe to have better access to our videos!

#livecellanalysis #livecellimaging #artificialintelligence