DTU will make it easier to analyze unique 3D images

Wednesday 19 Jan 22


Anders Bjorholm Dahl
Professor, Head of Section
DTU Compute
+45 45 25 39 07


  • MAX IV and ESS are two advanced research facilities in Lund, Sweden. The two plants use X-rays and neutron radiation, respectively, for advanced research experiments, including 3D imaging (imaging).
  • The MAX IV synchrotron is part of Lund University and is the first 4th generation synchrotron in the world. MAX IV is equipped with a Danish measuring station; the beamline DanMAX, where the equipment is adapted to samples, which must be examined with a resolution of 10-100 nanometers.
  • The Danish measuring station provides direct access for Danish researchers and companies to all the other measuring stations on MAX IV.
  • MAX IV was inaugurated in the summer of 2016.

European Spallation Source (ESS)

  • ESS will be the world's largest and most advanced neutron scattering facility. ESS is located in Lund.
  • The associated data center ESS Data Management & Software Center (DMSC) is located in Copenhagen.
  • ESS is ready in 2025.

Theme on health technology

Since 2010, the number of engineers in the healthcare system has increased by 22 percent, so that in 2019, 553 engineers were directly employed in the healthcare system. In a theme on health technology, DTU writes about developments in areas such as medical imaging technology, artificial intelligence and sensors, and portable equipment. Technology that supports doctors creates opportunities for faster diagnosis and treatment and increases quality.'
Machine learning must standardize 3D image analysis to improve use of data prepared on advanced research facilities.

Researchers and the industry have high expectations for the research facilities MAX IV and ESS in Lund in Sweden, where materials and systems can be examined down to the atomic level in 3D in the search for new discoveries in materials, medicine, and the environment.

With a grant of DKK 11.5 million from the Novo Nordisk Foundation, DTU is now starting to develop a standard procedure for how images taken on e.g. MAX IV can be analyzed using artificial intelligence. The work will take place through a new research infrastructure called QUAITOM.

"Today, the data analysis of 3D images often becomes a research project in itself, and it takes 10-100 times as long as everything else in the experiment. This puts a limit on how much benefit to receive from a research facility like MAX IV because data is not utilized as much as it could with better analysis techniques. We want to solve that problem,” says professor and section leader for Visual Computing at DTU Compute Anders Bjorholm Dahl.

He refers to the potential in the field of life science. In recent years, a strong research environment has been built up around 3D imaging, especially in life science with biomedical applications. Examination of tissue samples with 3D imaging makes it possible to see and understand structures very close to the structure that the tissue has in the living organism from which the samples originate. In addition, the tissue can be subsequently examined with histological techniques using light microscopy or electron microscopy.

The timing is perfect, as MAX IV will start research at two beamlines in 2022. The Danish DanMAX handles everything from life science to building materials, as well as ForMAX, adapted biomass experiments. In addition, the sister facility ESS, European Spallation Source, will also be ready for use soon.

Complicated 3D imaging
Artificial intelligence based on machine learning and especially deep learning has become the dominant approach to computer vision.

The basis for doing deep learning based on computer vision algorithms is that you have large datasets with thousands of images.

"With the QUAITOM platform, we get the muscles to grab the users themselves and take them to the methods we have created. Our research will then be used in a completely different way than before."
Anders Bjorholm Dahl, Professor & Head of section at DTU Compute

The researchers then develop the algorithm by showing it all the examples that it must learn to recognize, and when it has seen enough images, you can make something similar and make the algorithm recognize what the image contains. If there is a lot of variation, maybe millions of images are needed to make a usable algorithm. Therefore, areas where it has been easy to obtain data or where research groups have made large data sets have been studied in particular.

Data from 3D imaging is differently complicated because data lives in three dimensions, and you can extract many different types of information about the size and shape of the structures from the same image data. In addition, these are unique images where the researchers want to study the microstructure of materials, which they have not normally studied before, and they do not have access to images on which they can train the algorithm in advance. Typically, it will be a case where you look for something new and specific. But the variation within a data set will be relatively small.

“On the one hand, we have the problem that within 3D imaging there is a lack of methods for making effective analyzes. On the other hand, we have the research field of machine learning and deep learning, which works with relatively general analyzes that can provide very accurate information about the content of the images. We believe that there is a need to connect the two worlds and develop image analysis algorithms that can be trained with much less data,” says Anders Bjorholm Dahl.

Great potential in life science
The advantage of using imaging with X-rays or neutrons to examine the properties and structure of materials is that the samples remain intact. It has a wide range of applications in technical science and natural science as it makes it possible to record an image, change the sample, and record again. It is also possible to subsequently examine samples with other techniques.

Among other things, DTU has been involved in 3D-life science projects on understanding the brain's microstructural organization, how Covid-19 affects blood vessels in the heart, how peripheral nerves are affected by diabetes, and how muscle cells change in paralyzed patients.

“With MAX IV and ESS, we have two of the most advanced microscopes in the world, which makes it possible to make completely extraordinary experiments. It is still very complicated to analyze data coming out of such experiments, so it is crucial to be able to standardize the analysis of 3D images. A collaboration between researchers who create new AI-based image analysis methods and imaging researchers is unique, and holds great potential for world-class research results,” says Anders Bjorholm Dahl.

DTU's research becomes more visible
The data analysis platform will be a large computer that researchers can log on to and visualize, analyze and store 3D data. Users will be both people who make analyzes of their own data and people who will test their own algorithms on a data set provided by the platform. Including data for competitions in machine learning, where users must develop algorithms that solve specific problems.

Via the platform, DTU will also educate PhD students through workshops and PhD summer schools and, in the long term, master's students and others interested in 3D image analysis tools.

Finally, Anders' own Visual Computing research section will have a number of machine learning-based algorithms implemented on the QUAITOM platform, which people from outside can use to analyze their own data.

"It's quite amazing. When you as a researcher make an algorithm and get it published in a scientific article, then you can just hope that others will use it. With the QUAITOM platform, we get the muscles to grab the users themselves and take them to the methods we have created. Our research will then be used in a completely different way than before,” says Anders Bjorholm Dahl.

The grant from the Novo Nordisk Foundation extends five years.


  • QUAITOM - The Infrastructure for QUantitative AI-based TOMography
  • The QUAITOM platform will be part of the QIM center and will build on the experience and the network that has been built there.
  • With support from The Capital Region of Denmark, QIM was established in 2018 by DTU, the University of Copenhagen, Lund University, and MAX IV.
  • With QUANTUM, the data analysis and user collaboration in connection with MAX IV and ESS, which has started in QIM, will be expanded to also include collaboration in machine learning.
  • The collaboration will be based on a data analysis platform that provides optimal opportunities to develop the next generation of algorithms to optimize the comprehensive 3D measurements and data that are generated from the studies at the X-ray synchrotron and neutron facilities in Lund.
  • QUAITOM and QIM also cooperate with the new research facility 3D Imaging Center / DANFIX at DTU, where the 900 m2 laboratory will house 10 laboratory CT scanners when it is fully developed.
  • The Novo Nordisk Foundation supports QUAITOM with DKK 11.5 million over five years from the Data Science Research Infrastructure 2021 grant.

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