My research focuses on the development of deep learning algorithms for the automatic classification of cytoarchitectonic areas in high-resolution microscopy images of histological human brain sections.

The human brain can be subdivided into cytoarchitectonic areas, defined by the spatial organization of neuronal cells, including their distribution, size, type, orientation, as well as their arrangement into distinct cortical layers and columns. Cytoarchitectonic areas are indicators of brain connectivity and function, making them an important microstructural reference for multi-modal brain atlases. They can be analyzed based on high-resolution microscopic scans of histological brain sections, obtained by cutting postmortem human brains into thin slices and staining them for cell bodies.

The large inter-individual variability between brains necessitates the analysis of multiple brains to obtain a general picture of the human cytoarchitectonic organization. Modern high-throughput scanners allow large-scale acquisition of microscopic image data, but established cytoarchitecture analysis methods are not suitable for handling the resulting large amounts of data. This motivates the development of methods for automated cytoarchitecture analysis based on deep learning.

I develop deep learning methods to extract meaningful microstructural features from high-resolution microscopic images, which provide the foundation for automatic cytoarchitecture classification and the development of novel data-driven brain parcellations. I use contrastive learning with convolutional neural networks to extract cytoarchitectonic features, and apply graph neural networks to integrate learned features with 3D brain topology. In addition, I develop HPC workflows to handle large amounts of image data, and develop applications that provide users a way to interactively apply the developed deep learning methods.