Automated Analysis of Evolutionary Experiments of Phytoplankton
There is a strong interest in understanding community assembly and dynamics. Experimental approaches using phytoplankton have proven to be extremely insightful to unravel underlying biological processes. Imaging flow cytometry is an emerging method becoming more and more popular in different fields. It allows us to capture changes within a community or population in a more efficient way. As part of this project, we develop interactive and AI-based approaches to automatically quantify these experiments. The tools we develop are independent of application and will support all kinds of researchers inside and outside of Helmholtz working with imaging flow cytometry.
Other Collaborations
Connecting membrane pores and production parameters via machine learning (COMPUTING)
Isoporous block-copolymer membranes play a fundamental role in the filtration of liquids and can, for example, be used to purify drinking water. Despite recent progress in understanding the membrane formation process, finding suitable production parameters for a given precursor material (such as polymers of a certain length) still occurs in a trial-and-error fashion, wasting materials, […]
Extracting clinically relevant parameters from real-time MRI images of fontan hearts
Obtaining accurate segmentations of the heart in real-time MRI allows a more realistic view on clinically relevant parameters, such as the stroke volume. Cardiac real-time MRI can assess diastolic filling under breath maneuvers or other cardiac load situations which potentially enhances diagnostics other than CINE breath hold cardiac MRI. Real-time MRI allows rapid acquisitions during […]