Insights from M&M 2019

Observations from a ZEISS executive leader on one of the largest microscopy conferences in the world

The annual Microscopy & Microanalysis (M&M) conference was recently held in Portland, Oregon, USA. ZEISS is one of the largest sponsors and exhibitors at this meeting for the Microscopy Society of America that is dedicated to the promotion and advancement of techniques and applications of microscopy and microanalysis in all relevant scientific disciplines.

Allister McBride, Senior Director at ZEISS, at M&M 2019

In attendance was Allister McBride, a senior director at ZEISS who is responsible for materials research strategy. This includes understanding new trends in the market, translating those into customer needs, and working with R&D to create innovative solutions. Allister provided the following commentary on what he felt were some of the more interesting new trends and topics at this year’s M&M.

Overarching themes observed at M&M

This year’s M&M conference really impressed with the quality of the conference talks. This was clearly visible as many of the conference rooms were standing room only. The conference themes this year have moved away from discussing incremental instrumentation hardware improvements and were much more related to direct megatrends such as batteries, additive manufacturing and advanced material characterization. Many of the presentations discussed API driven automated experiments across the microscopy spectrum involving in situ rigs or 3D/4D analytical techniques.

4D analytics using LabDCT analysis of sintered copper particles representing the development of grain boundaries with heating. This data was taken from S.A. McDonald et al., Scientific Reports 7, 5251 (2017) and presented as an example in P13.1 ‘Advanced Characterization of Components Fabricated by Additive Manufacturing’

Multidimensional and multimodal characterization

There was a definite theme of performing multidimensional and multimodal characterization using different modalities. For examples, work done in talk 616 “Nondestructive 3D Nanoscale X-Ray Imaging of Solid Oxide Fuel Cells in the Laboratory” from the Colorado School of Mines and talk 617 “High Resolution 3D and 4D Characterization of Microstructure Formation in Novel Ti Alloys for Additive Manufacturing” from RWTH Aachen gave excellent examples of how this technique is used across two megatrend topics of solid oxide fuel cells (SOFC) and additive manufacturing research, respectively. In line with the theme of multimodal analysis, ZEISS took the opportunity to provide an update on its Secondary Ion Mass Spectrometry (SIMS) developments using neon ions as the secondary ion source.

SIMS image of a BAM L200 reference sample. (a) Total ion count (TIC). (b) Composite image of aluminum (red) and gallium (green). (c) Line profile of aluminum layers with corresponding SIMS map and schematic layout of the sample. The lines with a distance of 17.5 nm are resolved.

It really is a fantastic technology which is now enabling spatial resolutions of 15 nm as shown by the University of Cambridge in the poster “Analytics on the FIB: ORION-SIMS and the Discovery of a Unique, Chondrite-like, Precambrian Impactor.”

Combining multidimensional imaging with machine-based learning

Examples combining multidimensional characterization in a correlative manner were also demonstrated and this, combined with advanced segmentation techniques (EG. Session A02.1 – Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy), produced some interesting results which could not have been achieved without the combination of multiscale, multidimensional correlated imaging combined with machine learning based multichannel segmentation. This has recently become a hot topic as the power of machine learning to simultaneously segment datasets of various origins across length and dimensional scales is finding relevance in many diverse fields (e.g. A05.7-806 – “Projecting into the Third Dimension: 3D Ore Mineralogy via Machine Learning of Automated Mineralogy and X-Ray Microscopy”).

X-ray microscopy

X-ray microscopy was also a large theme across the board which shows that the conference itself has grown much broader than its initial roots in scanning electron microscopy. It was really fascinating to see how the resolution of this technique is increasing with a great example showing spirals forming from eutectic solidification in work by the University of Michigan (A05.4-336 – “Formation of Faceted Spirals during Directional Eutectic Solidification”) at ~50 nm resolution.

Overall, the depth and breadth of topics covered resonated extremely well with technology investments that ZEISS has made over the past several years and it was great to see customers using these capabilities in new and interesting ways.

A great example of this was talk A10.P2-1167 – “A Fast and Accurate Workflow for Analytic 3D FIBSEM Tomography” which was a collaborative talk between ZEISS and The University of Plymouth using the ZEISS Crossbeam FIB-SEM.

ZEISS Crossbeam in use in the ZEISS booth at M&M 2019
  • Read more about the new capabilities of ZEISS Crossbeam featured at M&M 2019.

Read Next – More Articles on Materials Research

Topic Materials Research

Blocking Radiation in Wearable Devices

Read article

Peering Inside the Solidification Process of Metals

Read article

Microscopy Techniques for Energy Materials

Read article

Investigating Cements for Endodontology

Read article

Improving Efficiency in the Materials Lab – Part 1

Automated Image Segmentation with ZEISS ZEN Intellesis

In this series, we are introducing the newest modules of ZEISS ZEN core – the most comprehensive suite of imaging, analysis, and data connectivity tools for multi-modal microscopy in connected material laboratories.

ZEISS ZEN Intellesis for simpler, automated image segmentation

Before starting analysis, the hardest step is always segmenting your images – dividing one region from another. If you can’t segment the regions automatically, the segmentation becomes the bottleneck for analysis. And that might end in a drop in productivity.

The ZEISS ZEN Intellesis module uses a simple interface – the user labels a few regions just by painting them in. Using deep learning, the software then labels the entire image. By labelling more regions, you can improve the model and teach the system how to segment your images properly. And best of all, once you’ve trained a model – you can re-use it, share it and use it for a bundle of images.

ZEISS ZEN Intellesis is part of the ZEISS ZEN core suite. It is compatible with all ZEISS standard analysis modules, third party images from any microscope, even 3D data sets.

More information on ZEISS ZEN core

Download a free trial version of ZEISS ZEN Intellesis

Read Next

A Magical Formula for Neuron Detection?

Leveraging ZEISS ZEN Intellesis and the digital microscopy platform APEER to automate segmentation workflows

Making sense of your imaging data traditionally was and mostly still is a tedious process. One of the baselines of any neuroscientific research: Neuron detection and segmentation.

Whether you are aiming to map brain structures in 3D, find out about the wiring of different parts of the brain or understand the defects diseases like Alzheimer’s are causing within the brain structures – it all starts with neuron detection and segmentation.

Only with a good segmentation in your hands, the extraction of relevant statistical data becomes possible.

After running the initial image through the workflow, you will receive a segmentation like this in our APEER account.

Learn how to automate neuron segmentation with a deep learning approach on the digital microscopy platform APEER

More information on ZEISS solutions for neuroscience

APEER is a digital microscopy platform for applications in science and industry. It enables you to customize image processing workflows for your specific job – all the way from image acquisition to reporting.

Read Next – More Articles on Neuroscience & Brain Research

Topic Neuroscience & Brain Research

Making Pathology Research More Efficient

Read article

New Light Sheet Microscope for Multiview Imaging of Large Specimens

Read article

New Multiplex Mode for ZEISS Airyscan 2 Enables Fast and Gentle Confocal Microscopy

Read article

Why Do Dopamine Neurons Die Particularly Fast in a Specific Brain Area?

Read article

Efficient Microstructure Characterization of Metals Using Light Microscopy

Your questions answered

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, especially for new materials or materials with multiple phases. For instance, magnetic materials being developed for use in electric motors consist of complex structures. Segmentation of these structures in different phases can prove difficult with traditional image analysis techniques.

High Temperature Corrosion Scale on 9% Chromium Steel. Left side (background): Brightfield image; Right side (foreground): individual layers segmented with machine learning.

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.

Watch the webinar on demand by registering here

Discover Q&A highlights from the live event

Read Next – More Articles in Materials Research

Topic Materials Research

Blocking Radiation in Wearable Devices

Read article

Peering Inside the Solidification Process of Metals

Read article

Microscopy Techniques for Energy Materials

Read article

Investigating Cements for Endodontology

Read article

Introducing ZEISS ZEN Intellesis: Deep Learning for Microscopy

Bringing our customers new solutions through digitalization

By adding robust new capabilities like deep 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.

ZEISS ZEN Intellesis: Advanced Image Segmentation

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.

One of the principal challenges of microstructural imaging has been that these techniques are challenging to scale and automate, usually because the continuous outputs of the imaging techniques have to be ultimately classified into discrete phases for subsequent analysis and interpretation. These image outputs are subject to a variety of artifacts and noise that cause traditional analytical techniques to fail as the images become more complex.

During visual examination, the brain of a trained microscopist acts to integrate the rich, potentially multimodal datasets to extract the desired information. Such an approach is challenging to capture and express in a computational form, making microscopy challenging and expensive to scale across the many 1,000s of samples that may be required to upscale and contextualize research results. Deep learning techniques give us, for the first time, a powerful set of tools to capture the complex set of processes involved in analyzing the rich datasets available to microscopic imaging in a way that is computationally scalable to a much larger range of samples.

Low contrast mining mineralogy grains, imaged using reflected light microscopy Left: Unclassified; Right: Classified with machine learning

Firstly, deep learning based-classification schemes are much more noise tolerant than their traditional counterparts, and single high fidelity datasets can be used to train classifiers operating across wide areas of a sample. Even more exciting than this, deep learning can be used to discriminate features that have little or no difference in their greyscale values, but instead have differences that are discriminated by textural features alone. They can also be used to drive analysis based on spatially registered data integrated from multiple microstructural analysis techniques.

We have already explored and developed a range of different applications for deep learning technologies for geological, mineralogical and metals microstructural examination. Additional development is underway for life sciences, materials science and routine laboratory applications.

Download a 30-day free trial of ZEISS ZEN Intellesis here

Read Next

Topic Geoscience

Exploring the Dawn of Animal Life

Read article

High Performance Geological Investigation

Read article

Connecting Information Across Dimensions With the New ZEISS ZEN Connect Imaging Software

Read article

Introducing a New X-Ray Micro-Computed Tomography (microCT) System

Read article