8 new Helmholtz Imaging interdisciplinary research projects funded

Each year Helmholtz Imaging publishes a call for interdisciplinary cross-center research projects that aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science. Current research projects represent a very broad variety of topics, for example trying to predict the space weather to developing a method for clinically diagnosing neurodegenerative diseases.

The new projects will begin over the course of next year and will deliver first results by the end of 2023. Topics include AI-based organoid formation, tools to quantify coastline change rate at high resolution and an automated image-based system to assess larval development.

Cross-disciplinary and cross-center research teams are now collaborating in a total of 24 Helmholtz Imaging projects including researchers from all the 18 Helmholtz Centers and all Helmholtz research fields.

Here is an overview of the new projects:

• Deep4OM (HMGU, DKFZ): Deep4OM aims to develop a deep learning-based framework for optoacoustic mesoscopy image analysis, enabling quantification of human skin biomarkers for non-invasive skindisease diagnosis.
• AIOrganoid (Hereon, HMGU): AIOrganoid will apply cutting-edge imaging techniques and novel AI-based solutions to facilitate human lung organoid formation, bridging the gap between cell biology and computational imaging.
• Benign (KIT, HZDR (NCT)): The goal of the Benign project is to develop a new optical imaging method that enables non-invasive molecular imaging at cellular resolution several mm deep in vivo.
• Highline (FZJ, DZNE): Magnetic resonance images of roots and vessels are very similar: both display thin, line-like objects. The aim of the project is to increase image quality of both kind of magnetic resonance data by exploiting their similarity.
• AutoCoast (Hereon, DLR): Coastal erosion enhanced by climate change has become an increasing global threat, which requires rapid detection and reliable risk assessment. The project aims to provide advanced AI tools for that.
• EMSIG (FZJ, KIT): To unlock to date unavailable insights into single microbial cells, the minimal units of functional life, EMSIG brings smart real-time event detection capabilities to microfluidic live-cell imaging.
• DIPLO (GEOMAR, AWI): DIPLO aims at developing a user-friendly software platform to analyze plankton images independent of the instrument with which raw data was collected. This will help to compare data and create a common database.
• ImageTox (CISPA, HZI (HIPS)): ImageTox focuses on the automated image-based analysis of zebrafish larvae development during toxicological studies to set up a fast, unbiased and commercially usable platform.

Stay tuned!