ZEISS Microscopy


Using Machine Learning to Transform Segmentation in Raw Materials

Great technological progress has been made over the last 20 years in the development of pore-scale imaging and modelling to address challenges in the geosciences and other pore-dominant samples. One of the principal challenges has been that these techniques are challenging to scale and automate, usually because the continuous outputs of the imaging techniques in question have to be ultimately classified into discrete phases for subsequent analysis and interpretation.


  • Use of quantitative performance metrics
  • How machine learning techniques perform when compared to more traditional image processing
  • Segmentation and analysis techniques for a suite of different images, including both X-ray microscopy and nano-scale FIB-SEM
  • How machine learning can help to discriminate features which have little or no difference in their greyscale values
  • A range of different applications of machine learning technologies to geological microstructural examination

Date: Saturday, June 6, 2020 2:30 PM - 3:30 PM SGT
Speaker: Matthew Andrew (ZEISS)

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