Bioluminescence imaging has become an indispensable technology in modern biological research, offering high sensitivity, a wide dynamic range, and the ability to visualize cellular processes in living systems with minimal phototoxicity and negligible excitation-induced background. Yet conventional approaches are fundamentally constrained by their reliance on exogenously supplied substrates such as luciferin. Because these substrates are gradually consumed and can distribute unevenly in tissues, signal intensities decay over time and spatial uniformity is compromised, complicating long-term, quantitative imaging—particularly in three-dimensional samples…

🔬👨‍💻 How Generative Artificial Intelligence Impacts BioImage Data Science. Join us for an engaging talk by Robert Haase (Global BioImage Analysis Network, GloBIAS, University of Leipzig) on March 6 at the Faculty of Health and Medical Sciences, UCPH. In this talk we dive into Large Language Models (LLMs) and Vision Language Models (VLMs) for Bio-image Analysis. We will learn about their capabilities for object counting, pointing at objects and drawing bounding boxes around them. We also dive into VLMs deciding…