Master Thesis Projects

We welcome driven students with a background in physics, nanobiology, microscopy, or data science who want to work at the interface of computational imaging and biomedical research - either on the experimental side (microscopy, hardware, tissue preparation) or on the computational side (image processing and signal analysis).

If you’re interested in a Master thesis project, please contact Miriam Menzel for possible projects.

Unfortunately, we’re not offering any BEP projects until end of 2024.

Computational Scattered Light Imaging (ComSLI) resolves nerve fiber pathways and their crossings with micrometer resolution. While other scattering techniques raster-scan the tissue with a light beam and measure the distribution of scattered light behind the sample, ComSLI uses a reverse setup: The whole tissue section is illuminated from many different angles and the normally transmitted light is measured, thus enabling much higher resolutions and requiring only standard optical components (LED light source and camera). This makes ComSLI a highly promising imaging technique, enabling to disentangle complex fiber structures - not only in brain tissue, but also in other biological tissues with comparable fiber structures (e.g. muscle or collagen).

Master Thesis Projects (MEP) - Examples

Start: any time (preferably in 2025)

Combining fluorescence and scattered light imaging

Computational Scattered Light Imaging allows to distinguish intricately entangled fibers (nerves, collagen, etc.) and their intersections at micrometer scale. Whole-slide fluorescence microscopy enables high-throughput scanning and digitization of an entire microscope slide. It has significant applications in pathology, allowing for rapid screening and analysis of tissue samples. In this project, we will develop an imaging system that combines fluorescence and scattered light imaging.

Analyzing different types of fibers

Up to now, Computational Scattered Light Imaging has mostly been used to reconstruct nerve fiber pathways in brain tissue, but it can also be applied to other fibrous structures like muscle or collagen fibers. In this project, we study ComSLI measurements of various biological tissue samples, and compare the scattering signals obtained from nerve, muscle, and collagen fibers. In this way, we want to better understand how these different types of fibers can be distinguished. Another aim is to distinguish tumor from healthy tissue. Depending on your interests, the project can be more software-focused (exploiting machine learning approaches for tissue classification) or experimentally-focused (modifying measurement parameters for enhancing the scattering contrast of non-neuronal structures).

Analyzing fiber sizes using different wavelengths

It is expected that the scattering of light depends on the feature size relative to the wavelength. In this project, we will systematically compare scattering signals obtained from measurements with different wavelengths on various tissue sections, to better understand how they are related to the underlying fiber structures, and how these measurements can be used to estimate the fiber sizes.

Confidence map for scattered light imaging measurements

Currently, the measurement results are mostly compared qualitatively, making it difficult to optimize measurements and judge differences between samples. In this project, we will develop a quality measure to better quantify the measured scattering signals and develop a confidence map to indicate the reliability of computed fiber orientations, taking regional information into account.