Robert Krueger, PhD
Assistant Professor
Department of Computer Science & Engineering at NYU Tandon
Robert Krueger is an assistant professor at New York University (NYU) - Department of Computer Science and Engineering, and a member of the NYU Visualization Imaging and Data Analysis Center. Previously, he was a postdoctoral research fellow and subgroup leader at VCG Harvard, School of Engineering and Applied Sciences at Harvard University, and a senior research scientist at the Laboratory of Systems Pharmacology, Harvard Medical School. Dr. Krueger received his Ph.D. degree (Dr. rer. nat.) in Computer Science at the Institute for Visualization and Interactive Systems, University of Stuttgart in 2017. Krueger's research interests lie in the field of data visualization and visual analytics for spatial and spatially-referenced multivariate data with a focus on biomedical visualization.
From Scalable Web-Based Rendering to Cell-Cell Interaction Analysis of Multi-Volume Tissue Data
Talk Title
Talk Description
3D Immunofluorescence imaging technologies enable novel insights into tissue microenvironments. The resulting high-resolution and multi-volumetric data provides a more detailed representation of the cells' shapes, formation, and interaction patterns. Instead of quantifying protein expressions in a low-resolution 2D tissue slice, researchers can now explore and study the 3D spatial distribution of proteins in-depth. I will address two challenges of visualizing and analyzing such data. Firstly, as the data is big, it cannot easily be loaded and processed as a whole, especially in web-based settings. I will present a hybrid multi-volume rendering approach based on a novel 'Residency Octree' data structure. It combines the advantages of out-of-core volume rendering using page tables with those of standard octrees to load data on-demand as needed for rendering the current scene and resolution in the user's viewport. Secondly, building on a multi-volume viewer, I will present a visual analysis interface to study cellular interactions in the multi-volumetric data. Instead of inferring interaction from the proximity of cells, we quantify interaction by spatially tracing the presence and levels of specific proteins from cell to cell in the 3D volume. We evaluated our application with biomedical experts who investigated T-cell activation in tumor microenvironments of human tissue data.