Eric Moerth, PhD
Research Fellow in Biomedical Informatics
Harvard Medical School - Department of Biomedical Informatics (DBMI)
Eric Moerth is a Post Doctoral Specialist Research Fellow in the Department of Biomedical Informatics at Harvard Medical School (DBMI) and part of the HIDIVE Lab. He received his PhD from the University of Bergen in Norway. During his PhD study, Eric Moerth conducted research in multimodal medical visualization. His focus was the research of new and innovative ways to visualize and explore medical data. Now Eric is researching the visualization of large-scale 3D Tissue Images using novel rendering techniques. Furthermore, his research enables the efficient analysis of highly multiplexed tissue images, and he also investigates how novel output devices like virtual and augmented reality devices can help investigators make better sense of their data.
3D Tissue Maps: Integrated Visual Analysis of Spatial Single-Cell Data, Segmentations, and Derived Metrics on Desktop and in Extended Reality
Talk Title
Talk Description
Advancements in highly multiplexed spatial single-cell imaging have enabled unprecedented insights into tissue organization and cellular interactions. However, integrating and analyzing complex spatial datasets, segmentations, and derived features remains a challenge. We present a novel approach for combined visual analysis of 3D tissue maps that integrates single-cell spatial data, segmentation results, and derived measurements in both desktop environments and Extended Reality (XR). Our framework enables intuitive exploration of tissue architecture, facilitating detailed connectivity analysis and multi-scale comparisons. By leveraging interactive 3D visualization and immersive XR interfaces, users can seamlessly transition between traditional screen-based analysis and immersive spatial exploration, enhancing their ability to identify patterns and relationships within complex biological systems. We demonstrate our approach using highly multiplexed tissue datasets, highlighting its potential for accelerating hypothesis generation and data-driven discoveries in spatial biology.