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!

Aivia_Neuroscience-VBE comparison mouse-1_traced_ROI

Accelerating Neuron Image Analysis with Automation

The ability to examine complex neural processes relies on the accurate reconstruction of neuronal networks at scale. Most data extraction methods in neuroscience research are time-consuming and…

Save Time and Effort with AI-assisted Fluorescence Image Analysis

The powerful synergy of THUNDER and Aivia analyze fluorescence images with greater accuracy, even when using low light excitation.

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…

Analysis of anatomy and axon orientation of an adult mouse brain tissue with QLIPP.

Learning the Cellular Architecture from its Optical Properties

In the last 3 years, microscopists have started to use "AI based" solutions for a wide range of applications, including image acquisition optimization (smart microscopy), object classification, image…

AI in Microscopy Webinar

We demonstrate residual channel attention networks for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data.