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:

• Segmentation based on machine learning
• Dictionary-based segmentation
• Graph-based segmentation

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.

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