Leica Microsystems: Science Lab

Science Lab

Das Wissensportal von Leica Microsystems bietet Ihnen Wissens- und Lehrmaterial zu den Themen der Mikroskopie. Die Inhalte sind so konzipiert, dass sie Einsteiger, erfahrene Praktiker und Wissenschaftler gleichermaßen bei ihrem alltäglichen Vorgehen und Experimenten unterstützen. Entdecken Sie interaktive Tutorials und Anwendungsberichte, erfahren Sie mehr über die Grundlagen der Mikroskopie und High-End-Technologien - werden Sie Teil der Science Lab Community und teilen Sie Ihr Wissen!

THY1-EGFP labeled neurons in mouse brain processed using the PEGASOS 2 tissue clearing method, imaged on a Leica confocal microscope. Neurons were traced using Aivia’s 3D Neuron Analysis – FL recipe. Image credit: Hu Zhao, Chinese Institute for Brain Research.

Unlocking Insights in Complex and Dense Neuron Images Guided by AI

The latest advancement in Aivia AI image analysis software provides improved soma detection, additional flexibility in neuron tracing, 3D relational measurement including Sholl analysis and more.

AI-based workflow for fast rare event detection in living biological samples using Autonomous Microscopy powered by Aivia

AI Microscopy Enables the Efficient Detection of Rare Events

Localization and selective imaging of rare events is key for the investigation of many processes in biological samples. Yet, due to time constraints and complexity, some experiments are not feasible…

Left-hand image: The distribution of immune cells (white) and blood vessels (pink) in white adipose tissue (image captured using the THUNDER Imager 3D Cell Culture). Right-hand image: The same image after automated analysis using Aivia, with each immune cell color-coded based on its distance to the nearest blood vessel. Image courtesy of Dr. Selina Keppler, Munich, Germany.

Accurately Analyze Fluorescent Widefield Images

The specificity of fluorescence microscopy allows researchers to accurately observe and analyze biological processes and structures quickly and easily, even when using thick or large samples. However,…

Using Machine Learning in Microscopy Image Analysis

Recent exciting advances in microscopy technologies have led to exponential growth in quality and quantity of image data captured in biomedical research. However, analyzing large and increasingly…

Separation of cells based on their tracking status: A colourised binary mask of a time-lapse microscopy field of view of medium confluency with individual cells highlighted as survivors if they can be tracked since the initial movie frame (cyan), incomers if they migrated into the field of view throughout the movie (yellow) or mistracks if an error occurred in the automated trajectory reconstruction (red).

Tracking Single Cells Using Deep Learning

AI-based solutions continue to gain ground in the field of microscopy. From automated object classification to virtual staining, machine and deep learning technologies are powering scientific…

How does an Automated Rating Solution for Steel Inclusions Work?

The rating of non-metallic inclusions (NMIs) to determine steel quality is critical for many industrial applications. For an efficient and cost-effective steel quality evaluation, an automated NMI…

How to Conduct Standard-Compliant Analysis of Non-Metallic Inclusions in Steel

This webinar will provide an overview of the significance of non-metallic inclusions in steel and outline the important global standards for rating the quality of steel and difficulties that arise in…

Challenges Faced When Manually Rating Non-Metallic Inclusions (NMIs) to Determine Steel Quality

Rapid, accurate, and reliable rating of non-metallic inclusions (NMIs) is instrumental for the determination of steel quality. This article describes the challenges that arise from manual NMI rating,…

Analyzing Non-metallic Inclusions in Steel

Oftentimes we find ourselves caught up in tedious analyses by reticle and comparison chart, time-consuming double-evaluation according to several standards or subjective inspection results with a bias…