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Quantitative analysis of a sample’s microstructure is based on image segmentation, but dividing the image into constituent objects of interest and their local background is a challenge for scientists. Classical function-based automation has its limits for classifying images. 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 and gain insights. The artificial-intelligence-powered pixel classifier provides reproducible segmentation results fast and overcomes human action. It delivers more robust results compared to classical function-based automation. And here is why …
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