Alexander Oberstraß

PhD Student

Research Topics

Machine Learning, Deep Learning, Medical Image Processing, Virtual Staining, Self-Supervised Learning, High-Performance Computing

Contact

+49 2461/61-5960

+49 2461/61-3483

E-Mail

0000-0003-0712-034X

Address

Forschungszentrum Jülich GmbH
Wilhelm-Johnen-Straße
52428 Jülich

Institute of Neurosciences and Medicine (INM)

Structural and Functional Organisation of the Brain (INM-1)

Building 15.9 / Room 3028

You can find us here

About me

As a researcher and software engineer, I perform AI-based analysis and develop software solutions for real-time visualization of large-scale scientific imaging datasets in a high-performance computing environment. Specifically, I work on tiamat, a Python toolkit for accessing high-resolution and multi-scale images in configurable image processing pipelines. My research focus is on self-supervised learning of interpretable descriptors of fiber architecture in three-dimensional polarized light imaging and atlas integration of derived measures.

My work contributes to the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) and the Europe's Digital Infrastructure for Brain Research (EBRAINS).

Publications

Oberstrass, A., Vaca, E., Upschulte, E., Niu, M., Palomero-Gallagher, N., Graessel, D., Schiffer, C., Axer, M., Amunts, K., & Dickscheid, T. (2026). From fibers to cells: Fourier-based registration enables virtual Cresyl violet staining from 3D polarized light imaging. Imaging Neuroscience, 4, IMAG.a.1079. https://doi.org/10.1162/IMAG.a.1079

Oberstrass, A., DeKraker, J., Palomero-Gallagher, N., Muenzing, S. E. A., Evans, A. C., Axer, M., Amunts, K., & Dickscheid, T. (2024). Analyzing Regional Organization of The Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI56570.2024.10635467

Oberstrass, A., Muenzing, S. E. A., Niu, M., Palomero-Gallagher, N., Schiffer, C., Axer, M., Amunts, K., & Dickscheid, T. (2024). Self-supervised representation learning for nerve fiber distribution patterns in 3D-PLI. Imaging Neuroscience, 2, 1–29. https://doi.org/10.1162/imag_a_00351

Last Modified: 13.04.2026