Enhancing 3D Spatial Biology with AI: Simplified Insights for All

Won Yung Choi, Ph.D.

Product Owner & Manager – Data & Analysis, Leica Microsystems

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Won Yung Choi is the Product Owner and Manager for Aivia AI Image Analysis Software at Leica Microsystems, responsible for the roadmap and feature implementation. Won Yung received her Ph.D. from Columbia University, New York, in dopaminergic circuitry changes during learning. In the last two decades, Won Yung has been involved in advanced microscopy and image analysis, starting as a postdoc researcher at Columbia, then most recently as the National Sales and Support Manager of Imaris Americas (Oxford Instruments) before joining Leica to drive R&D and create smarter AI-driven image analysis solutions to get from eye to insights faster.

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Hoyin Lai

Application Development Manager, Leica Microsystems

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Hoyin studied Bioengineering at the University of Washington, Seattle, USA, where he designed a single-stroke peristaltic pump for fluid delivery in microfluidic devices. He joined the Aivia team in 2010 as Application Engineer. Since launching Aivia in 2017, Hoyin has focused on applying artificial intelligence (AI) techniques to develop image analysis solutions for life science researchers.

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Unlock advanced insights into 3D spatial biology using AI without coding: learn to segment, phenotype, and analyze complex tissue images easily with Aivia.

In this webinar, you will learn:

• 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

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 (up to 15). 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.