Tag Archives: Machine Learning

Efficient microstructure characterization of metals using light microscopy

Your questions answered

Microstructure of Material

A material’s properties are strongly linked to its microstructure, such as grain size, porosity, phase and non-metallic inclusions. Light microscopy is a powerful tool for evaluating a material’s microstructure, but extracting meaningful results using traditional image analysis can be challenging.

In a recent SelectScience® webinar, Tim Schubert, materials scientists at the Materials Research Institute Aalen (IMFAA), Aalen University, and Torben Wulff, solutions manager light microscopy at ZEISS Research Microscopy Solutions, introduce a new comprehensive solution for microstructure analysis and present standardized techniques for metallography investigation.


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Introducing ZEISS ZEN Intellesis: Machine learning for microscopy

Bringing our customers new solutions through digitalization

By adding robust new capabilities like machine learning to our microscopy systems, we are initiating a step-change in the way our customers in industry and academia manage and process vast amounts of imaging data generated by a range of imaging modalities. This enables them to easily and intelligently obtain scalable, quantitative insight.

The first algorithmic solution introduced by the ZEISS ZEN Intellesis platform makes integrated, easy to use, powerful segmentation for 2D and 3D datasets available to the routine microscopy user. ZEISS ZEN Intellesis software is available for the company’s full range of optical, confocal, X-ray, electron and ion microscopes using the ZEISS Efficient Navigation (ZEN) platform.


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