3D Spatial Biology Insights Simplified with AI

Come learn how AI-powered segmentation, spatial analysis, and phenotyping can help you gain new insights for 3D images with complex morphological features and multiple biomarkers. With Aivia you can characterize the tissue microenvironment, examine differences between normal and disease tissues, and much more without the need to code or train deep learning models.

Join us for a journey into the realm of 3D spatial biology. See Aivia’s enhanced deep learning model accurately detect, up to 78% faster, and partition cells with morphological variations. Discover how to leverage your expertise or simple automation to classify cells into different phenotypes and interactively explore them in their spatial context. With a few clicks, you can get measurements such as percentage distribution and Pearson correlation coefficient as well as chart the relationship between biomarkers and clusters using dendrogram, dimensionality reduction and more.

Using Aivia's robust analysis tools you can confidently explore multiplexed 3D images in a spatial context.

Key Learnings:

• How to accurately segment 3D cells with different morphologies using AI   
• Leverage your expertise and AI to identify known phenotypes within your image   
• Explore unknown phenotypes with automatic clustering   
• Gain deeper spatial insights into your 3D tissue with dendrograms, violin plots, dimensionality reduction and much more 

Register here