“AI will revolutionize microscopy”

Zeiss is one of the technology leaders in the microscopy market. Dr. Michael Albiez became Managing Director of Carl Zeiss Microscopy GmbH and head of the Research Microscopy Solutions strategic business unit in late 2019. In the interview, he talks about the increasing importance of software and deep learning approaches in microscopy, about the drivers behind the many different microscopy methods, and the influence that the COVID-19 pandemic is having on the market for medical imaging.

Dr. Albiez, would you like to briefly describe Zeiss?

Dr. Michael Albiez: When Carl Zeiss established his workshop for optical instruments in Jena, Germany, in 1846, microscopy was the first business unit. These days, the Zeiss Group is a technology leader in the optical industry with 30,000 employees worldwide. Of these, 3,000 are employed in the field of microscopy. In addition to microscopy solutions for the life sciences and material research, Zeiss also develops solutions for industrial metrology and quality assurance, medical engineering, lithography optics to manufacture semiconductor components, as well as spectacle glasses, camera lenses and binoculars. As a foundation company, we plan for the long term. This enables us to translate advances in basic research into innovative products. We work closely together with scientific partners to test the limits of what is technically feasible. This began with the collaboration between Carl Zeiss and Ernst Abbe, when they developed the fundamentals of modern optics and created the requirements for manufacturing microscopes in a reproducible quality. This pioneering spirit is still very much alive in the company.

You are in charge of the Research Microscopy Solutions strategic business unit. To what extent is this strategic for Zeiss?

Albiez: Microscopy is very important for Zeiss because it has a special significance in optics. The Zeiss Group began with and developed from microscopy. Research Microscopy Solutions works very closely together with leading scientists. Many of our devices can be found in laboratories where cutting-edge research is carried out. Numerous Nobel Prize winners over the past decades have carried out their research on Zeiss microscopes. We provide scientists with the high-end technology that they need to break new scientific ground. Super-resolution microscopy is a good example. In all of this, the units in the Zeiss Group stimulate each other. For example, in the area of high-precision freeform optics which is a key technology for microscopy and medical engineering as well as for nanostructuring in semiconductor technology.

From simple light microscopes to confocal, electron/ion and X-ray microscopy, Zeiss offers a very wide range of products in this field. What are the advantages?

Albiez: Zeiss is the only company that covers such as wide spectrum of microscopy. Our customers benefit from the different technologies with which they can examine their samples with different techniques. They have very complex questions that cannot be answered with just one method. For instance, battery research uses several microscopy processes for microscopic and nanoscopic analysis of the active material distribution on electrodes and the aging process in batteries. This begins with nondestructive X-ray microscopy and continues with the specific selection of relevant areas using an electron microscope to carry out structural detailed analyses at exactly that point with nanometer accuracy. So that users are able to navigate through the different scales when changing methods, we offer them the correlative software platform ZEN Connect for a smooth workflow between macro, micro and nanoscopes with different methods. There is also demand from the life sciences—such as for cancer and Alzheimer's Disease research, drug development and pathology. Using correlative light and electron microscopy on a micro and nanometer level with, in some cases, different time resolutions, scientists are able to elicit an increasing amount of information from their samples. This is also because the different microscopes work at the limits of what is physically possible and fulfill increasingly special requirements.

These days, microscopes are complex systems with software and cameras. What are the drivers for the increasing digitalization in microscopy?

Albiez: Demands for speed, resolution, and contrasts have grown dramatically, as research increasingly has more specific questions. At the same time, microscopy must remain operable and deliver reliable, optimum results. Digitalization is the key that enables even non-professionals to handle the equipment. Because microscopy is used in more and more niche applications these days, not all users are specially trained. Automation and software ensure efficient workflows. Camera systems are needed, as data volumes are growing exponentially. Terabytes are generated even for simple questions. People cannot evaluate this volume of data. And most certainly not in brain mapping projects where the aim is to achieve nanometer precise mapping of the human brain. Automated processes are absolutely necessary for high-throughput analyses and analyses of large areas of investigation with nanometer precision.

This is where deep learning methods come into play. How would you estimate the future potential of artificial intelligence in microscopy?

Albiez: Artificial intelligence has enormous potential in microscopy. In particular, the images are expedient to find new answers to scientific questions, to prepare medical diagnoses, and to assess the reliability of components. Neural networks can filter these answers from huge volumes of data much faster and, in many cases, much more reliably than humans are able to. The methods are also increasingly used in pathology because the number of investigations is increasing a lot faster than the number of pathologists. Fast, reliable statements are wanted. Today, AI algorithms are not only used in fully automated image evaluation, we also use them to control the microscopes. They help users find relevant areas on slides and adjust the lens sharply, and they are also able to set the right contrast based on the sample. AI methods are also useful in sample preparation. In some cases, no fluorescent dye is required because the algorithms obtain relevant information from shapes and other sample-inherent properties and can implement subsequent virtual labeling. Pathologists will often not have to dye their samples any more, which minimizes time and costs for the chemical aids. We are still at the beginning in this area. AI will revolutionize microscopy—this concerns the traditional understanding of microscopy as well as the development of microscopes where the focus is increasingly shifting from hardware towards software. However, there is still much to do, especially to simplify training of the neural networks.

Do you believe that there are other fascinating technology trends in microscopy?

Albiez: These days, in many cases, software reduces the effort needed for hardware development. Whereas in the past the focus was on stronger light sources, now, thanks to improved software routines, in many cases less light is needed. This also has the benefit of causing less damage to the samples. For example, the trend is heading towards observing living cells for hours and days at the highest resolution. AI is an enormous help here in eliciting all the available information despite reduced contrasts and resolution. Basically, the aim is to achieve progress in the triangle of resolution, speed, and sensitivity in order to find more specific answers to scientists’ questions from increasing volumes of data. Think about Alzheimer’s Disease research: The aim here is to make large areas of the brain analyzable at a high resolution in order to understand changes on a micro-structural level. In many applications, scientists are searching for answers on a nano level to understand macro-structures. This requires automated high-throughput processes. Another trend is the increase of microscopy in new areas of application, such as personalized medicine, in which numerous startups are developing methods for new, individualized molecular diagnoses—using modern microscopic workflows. Covid-19 is another driver for this development. This is why over the past few months we have supported virus researchers specifically with expertise and, in some cases, also with loan equipment. In our company, the pandemic-related restrictions have triggered a digitalization push. We have supported our partners and customers as well as possible with remote service, webinars, and web-based training.

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