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Upcoming Events




Through these events, we aim to provide insights into how you can optimally acquire images, perform image QC, and correct any imaging artifacts using Huygens. This all with the aim to obtain scientifically more solid and quantitative results when using for example the Object Analyzer, Object Tracker and Colocalization Analyzer.

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Missed our latest webinar? Not to worry, you can watch it here.


WEBINAR 10-11-2026

AA DF

Live Webinar (30min): "Working with Extreme Large Datasets: from GBs/TBs to optimal results in Huygens".
Nov. 10th, 2026 at 9:30 and 18:00 CEST.
Struggling with stitching, deconvolving, and visualizing GBs or TBs of data? Interested in viewing large datasets on any computer/laptop?

If so, this webinar may be of interest to you. We’ll discuss how to stitch, deconvolve, and visualize datasets that are even larger than your computer’s RAM with Huygens. We’ll also show you how to work with Huygens’ recent Pyramid File Formats, save BigDataViewer files, and crop regions of interest from large datasets for further processing and analysis. The image shown above is from a 870 GB Zeiss Light Sheet Z.1 dataset that was stitched, vignetting-corrected, deconvolved, and rendered as a MIP using Huygens Professional. Raw data provided by Dr. Dominic Filion, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, Canada.


Register here

WEBINAR 8-12-2026

Peroxisome Movie End Fragment

Live Webinar (30min): "Acquire Images 20× Longer with less bleaching, healthier cells, and improved quality?".
Dec. 8th, 2026 at 9:30 and 18:00 CEST.
If you are a microscopist who's hear more interested in the how than the who, theis webinar is for you. Every microscopist is confronted with the pyramid of imaging trade-offs: balancing signal strength, acquisition speed, field-of-view, photobleaching, sample health, and spatial and temporal resolution. What if experimental showed how to push beyond these limitations - further than you may thought possible. Interested? Register for this webinar below.

Shown is a time series of peroxisomes acquired in collaboration with the Kapitein Lab, Cell Biology, Neurobiology and Biophysics, University of Utrecht, The Netherlands.
Register here