Science Lab

Benvenuti nel portale delle conoscenze di Leica Microsystems. Troverete materiale didattico e di ricerca scientifica sul tema della microscopia. Il portale supporta i principianti, i professionisti esperti e gli scienziati nel loro lavoro quotidiano e negli esperimenti. Esplorate i tutorial interattivi e le note applicative, scoprite le basi della microscopia e le tecnologie di punta. Entrate a far parte della comunità di Science Lab e condividete la vostra esperienza.

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…

H&E stained micrograph of an intramucosal esophageal adenocarcinoma (left) enhanced with Aivia’s Pixel Classifier (right)

Simplifying the Cancer Biology Image Analysis Workflow

As cancer biology data sets grow, so do the challenges in microscopy image analysis. Aivia experts cover how to overcome these challenges with AI.

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…

AI in Microscopy Webinar

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