Tackling the segmentation and tracking challenges of growing colonies and microbialdiversity
PhD biology students in the past would have to make the best of their monotonous lab tasks: “We would hold regular cell-counting parties,” relates Prof. Dr. Dietrich Kohlheyer of Forschungszentrum Jülich. “We had beer and pizza, and at recurring intervals we would look through the microscope to count how the bacteria were multiplying over time.” However, when it comes to analysing more complex cell structures, counting is no longer an option.
This is where the research project comes in, which is aimed at developing a deep-learning method for analysing images from microscopes. “We will then use this method to watch bacteria as they grow,” Kohlheyer says. As the bacterial cultures grow under the microscope, pictures will be taken at set intervals. The task goes beyond simple counting: “We also want to know how the individual cells behave. So, for example, what daughter cells came about from the division of a parent cell?” says Dr. Katharina Nöh. She and her workgroup at FZ Jülich are developing software that can be used to track a cell family tree of sorts from images chock full of bacteria. The method of choice is called “probabilistic multi-object-tracking”, and the data processing for this method is being developed together with Prof. Ralf Mikut of KIT and Dr. Hanno Scharr of FZ Jülich. It is intended to work for all kinds of morphologies. Different bacteria namely develop different structures as they grow: some have daughter cells that split off, some have vesicles that burst open, while others grow out long like the branch of a tree. The aim is to develop artificial intelligence that can follow all of these variants, and even distinguish between individual cells among several bacterial species living and growing together.
Geophysical Joint Inversion for Accurate Brain Myelin MappingThe aim of this project is to develop a method for clinically diagnosing neurodegenerative diseases. The content of myelin in the brain – a substance that becomes degraded in diseases – will be quantified using methods from geophysics in order to facilitate early detection and treatment.
Neuroimaging Biomarkers for Restless Leg SyndromeThe aim is to develop a software solution that can analyse enormous amounts of data on tens of thousands of subjects from large-scale health studies. Using restless leg syndrome as an example, genomic data will be combined with neuroimaging data in order to identify new biomarkers with the help of machine learning methods.
Advanced Soft-X-Ray Microscopy SolutionsThe project aims to develop a method that will speed up the analysis of diffraction patterns that arise in UV and soft X-ray light microscopy, so that the structure of the studied sample can be calculated more efficiently. The method could make the three-dimensional study of nanomaterials considerably easier. There are times when researchers need […]