Science Lab

Science Lab

ライカマイクロシステムズのナレッジポータルでは、顕微鏡の基礎から最先端技術まで、幅広い情報を提供しています。初心者から熟練者、研究者、医師の皆様まで、日々の研究や実験に役立つ内容となっております。チュートリアルやアプリケーションノートを活用し、学びながら探究心を刺激してください。さらに、コミュニティに参加することで、知見を共有し、新たな発見へとつなげましょう。お気軽に参加いただき、互いの専門知識を深め合う場としてご活用ください。
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The AI-Powered Pixel Classifier

Achieving reproducible results manually requires expertise and is tedious work. But now there is a way to overcome these challenges by speeding up this analysis to extract the real value of the image…

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…

Applying AI and Machine Learning in Microscopy and Image Analysis

Prof. Emma Lundberg is a professor in cell biology proteomics at KTH Royal Institute of Technology, Sweden. She is also the director of the Cell Atlas, an integral part of the Swedish-based Human…
Single timepoint of a drosophilia embryo, 3D object detection

Examining Critical Developmental Events in High-Definition

Extended live cell imaging of embryo development requires a delicate balance between light exposure, temporal resolution and spatial resolution to maintain cells’ viability. Compromises between the…
Single timepoint of a time-lapse recording of mammary epithelial micro spheroid cultured in 3D highlighting individual mitotic events

Observing Complex Cellular Interactions at Multiple Scales

Learn how to observe challenging cellular interactions with easy to deploy object detection and relationship measurements.
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…
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…
Dynamic Signal Enhancement powered by Aivia: Truly simultaneous multicolor imaging of live cells (U2OS) in 3D

Artificial Intelligence and Confocal Microscopy – What You Need to Know

This list of frequently asked questions provides “hands-on” answers and is a supplement to the introductory article about Dynamic Signal Enhancement powered by Aivia "How Artificial Intelligence…
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