Computational Clearing - Enhance 3D Specimen Imaging

May 08, 2020 Webinar

Computational Clearing - Enhance 3D Specimen Imaging

Free Webinar On-Demand

Computational Clearing, a new opto-digital imaging method, is helping researchers eliminate the "haze" or out-of-focus blur that, until now, has been typical for images of 3D specimens taken with traditional light microscopes. Computational Clearing efficiently differentiates between signal and background by taking the size of the targeted specimen features into account. This approach makes image details immediately visible which formerly were not revealed - with the speed and ease of a traditional light microscope, whether creating single images or capturing large image stack for 3D analysis.

This webinar is designed to clarify crucial specifications that contribute to THUNDER Imagers' transformative visualization of 3D samples and improvements within a researcher's imaging-related workflow.

Speaker

Evan Darling, Product Manager of Advanced Microscopy

Evan focuses his efforts on innovating products for the biological sciences. His education in Imaging Science and Biology at the Rochester Institute of Technology come together while working at Leica. He has spent time developing projects involving multi-spectral imaging systems, 3D-printing matrices for cellular regenerative studies, and STORM imaging, to name a few. Prior to joining Leica’s Product Management team, he was an Advanced Workflow Specialist working with labs to identify imaging system solutions for their unique specimen, application, and workflow needs. The novel research that Leica’s customers work on is wonderful to be a part of, and Evan is happy to innovate advanced microscopy solutions with them.

Key Learnings

The science behind Computational Clearing and how THUNDER Imagers work in a step-by-step protocol
Where to find additional information on Computational Clearing
Imaging possibilities with each THUNDER imaging method - 3D live cell, 3D tissue, and model organism - and understand how each differentiates itself

Interesting for

Molecular biologists
Developmental biologists
Cell biologists
Neuroscientists

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