Analysis of 3D Microstructure

Analysis of 3D microstructure is one of the core research areas within the Visual Computing section at DTU Compute. We develop new methods for fast and accurate quantification of 3D microstructure from large 3D volumes. Our research is conducted in close collaboration with scientists developing and using 3D imaging. Together, we promote 3D imaging as a reliable tool for measuring microstructure.

Thanks to the construction of the large-scale facilities MAX IV and ESS in Lund, Sweden, there is now a fantastic opportunity for 3D imaging at extremely high spatial and temporal resolutions close to DTU. An obstacle in fully utilizing this opportunity is a time-consuming image analysis, which is challenged by the size and complexity of the acquired data sets. There is therefore a need for expertise and new analysis tools for 3D imaging. Developing this expertise and associated tools is our research focus.

We have established the QIM: The Center for Quantification of Imaging Data in a collaboration with University of Copenhagen and Lund University. QIM is also a part of the DTU 3D Imaging Center - 3DIM, which is our X-ray µCT laboratory that is also tied closely to the DANMAX beamline at MAX IV.

Below, you find our research highlights:


Research core

Quantitative characterization of 3D images involves measuring the geometric and intensity characteristics of imaged samples. Often, the geometric features are of interest, because they determine the properties of a sample. This puts focus on methods for efficient and easy segmentation.


Hans Martin Kjer
DTU Compute
+45 45 25 30 35
Vedrana Andersen Dahl
Associate Professor
DTU Compute
+45 27 35 98 81
Marco Pizzolato
Assistant Professor
DTU Compute
Anders Bjorholm Dahl
Professor, Head of Section
DTU Compute
+45 45 25 39 07

Individual muscle fibers segmented from the 3D X-ray synchroton data. Segmentation allows for quantification of various features - here local thickness. A) A sample from a Healthy Control (H). B) A sample from a stroke patient (ST). C) A sample from a Spinal Cord Injury patient (SCI). D) The statistics of the three samples (Pingel et al. 2022).

Segmentation based on machine learning

If advantageous, we use machine learning and deep learning for segmentation, for example, when analyzing cardiac tissue as in Reichardt et al., 2021. To minimize the need for human input, we research in active learning.

Dictionary-based segmentation

Using InSegt to interactivelly segmenting indivudual cones of a bee eye in a slice from 3D X-ray CT.

Our dictionary-based segmentation Dahl et al., 2020 is based on machine learning principles. Both the the dictionary and the associated labels are learned from data. This enables automatic image segmentation, fast enough for real-time interaction. An outcome of this is the Insegt segmentation tool (code repository). InSegt has been used for numerous projects, and in particular for analysing microstructure of composite materials (Wang et al. 2021) but also compound bee eye (Tichit et al. 2022).


Graph-based segmentation

We have developed novel methods for graph-based segmentation (Jeppesen et al. 2020 and Jensen et al. 2020), implemented efficient graph-based algorithms (Jeppesen et al. 2021) and benchmarked existing graph-based algorithms (Jensen et al. 2022project page). Our methods have been used for analysing large 3D data, for example from samples of peripheral nerves (Dahlin et al. 2020) and muscle fibres (Pingel et al. 2022).

Segmenting individual foam bubbles in a 3D X-ray CT image of aluminium foam using approach from Jensen et al. 2020.


3D imaging and the analysis of 3D microstructure is central for DTU courses 02509 High-Performance Computing for Analysis of Experimental 3D Imaging Data and 02510 Deep Learning for Experimental 3D Image Analysis. Relevant tools are also taught in 02506 Advanced image analysis.

External references

If you are interested in an MSc, BSc, or other student projects in this area, you are welcome to see our list of student projects.

We are engaged in industry collaboration within the assciation for Linknig Industry to Neutron and X-rays LINX and Lund Institute of advanced Neutron and X-ray Science LINXS.

Industrial collaborators

ROCKWOOL, Tetra Pak, Grundfos, Velux, Haldor Topsøe, Novozymes, Novo Nordisk, Arla.