Helmholtz Imaging Projects aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.
Funds for Helmholtz Imaging Projects are annually granted to cross-disciplinary research teams for collaborative mid-term projects.
Ideally, Helmholtz Imaging Projects are co-created with users and non-academic stakeholders to ensure the quick adoption of results.
Funding for the first Helmholtz Imaging projects started in December 2020. Many teams have since begun work on major challenges and pressing issues facing society to develop sustainable solutions for tomorrow and beyond.
Discover these outstanding and fascinating research projects with us or become a part of Helmholtz Imaging Projects and apply for your own project. The new call will be published in spring 2024. Stay tuned!
Artificial Intelligence Assisted-Imaging for Creating High-yield, High-fidelity Human Lung Organoid
AIOrganoid will apply cutting-edge imaging techniques and develop novel AI-based solutions to facilitate human lung organoid formation with high yield and fidelity, bridging the gap between cell biology and computational imaging.
Advanced Soft-X-Ray Microscopy Solutions
The 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 […]
Automatic detection of coastline change and causal linkage with natural and human drivers
Coastal erosion enhanced by climate change has become an increasing global threat, which requires rapid detection and reliable risk assessment. AutoCoast aims to provide advanced and reliable remote sensing-based AI tools to quantify coastline change rate at high-resolution and unravel the linkage between coastline change rate and natural and anthropogenic drivers at regional to global scale.
X-ray tomoscopy of dynamic manufacturing processes
How can the manufacturing processes of materials be mapped at the smallest level? How do you train an artificial intelligence to analyze these processes automatically? That’s the focus of the Avanti project, which aims to improve X-ray tomoscopy – the imaging and quantification of three-dimensional images of very fast-moving processes.
Biocompatible and Efficient Nanocrystals for Shortwave Infrared Imaging
The BENIGN project aims to enable non-invasive molecular imaging with cellular resolution in vivo at depths of several millimeters. This will be achieved using light from the shortwave infrared (SWIR) range (1000-2000 nm), which has less scattering and autofluorescence compared to the visible and near-infrared spectral range. Bright and targeted imaging agents are needed to fully exploit this range. The project will develop a new approach using lanthanide-based core-shell structures that emit light in the 1500-2000 nm range.
Breaking resolution limit of electron microscopy for magnetic materials
A new method will make it possible to take images of the magnetic properties of materials under the electron microscope and to correlate these properties with their atomic structure. In order to achieve high resolution, a special algorithm must be developed to compute the magnetic properties from the microscope data.
Deep learning powered optoacoustic mesoscopy for non-invasive diagnostics of skin diseases
Deep4OM aims to develop a deep learning-based framework for optoacoustic mesoscopy image analysis, enabling quantification of human skin biomarkers for non-invasive skin disease diagnosis. Deep4OM has the potential to change the landscape of non-invasive skin imaging, and could significantly promote the diagnostic and prognostic applications of RSOM in clinical routine.
Paving the way from in situ plankton image data to a Digital Twin Ocean
This project will develop a user-friendly software platform to analyze plankton images independent of the instrument with which images were collected. This will help to compare data and create a common database, which is a critical step towards an image-based ecosystem component of a “Digital Twin Ocean”.
Event-driven Microscopy for Smart Microfluidic Single-cell Analysis
Microfluidic live-cell imaging (MLCI) unlocks spatio-temporal insights into population heterogeneity emerging from a single cell. EMSIG brings smart live-event detection capabilities to MLCI to facilitate the adaptive optimization of biological event resolution and autonomously counteracting deteriorating image qualities.
High Image Quality for Lines in MRI: From Roots to Angiograms
MR 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 MR data by exploiting their similarity. HighLine aims at obtaining high quality images in reduced scan time to lower patient burden and increase patient and plant throughput by adapting state-of-the-art 3D image enhancement methods, and developing new deep-learning based methods.
The Hidden Image of Thawing Permafrost
The project aims to develop a method for determining just how extensively thaw processes have already progressed in permafrost regions. The machine learning approach to be developed will be used to analyse radar images from aircraft in order to learn more about the properties of the subsurface permafrost.
Artificial Intelligence for 3D multimodal point cloud classification
The aim is to develop an artificial intelligence that can achieve the fusion of two-dimensional data with three-dimensional information. Based on this, the software would simultaneously be able to recognise image characteristics as well as the spatial relationships between different objects.
Automated image-based Detection of Early Toxicity Events in Zebrafish Larvae
ImageTox wants to establish an automated image-based system to assess zebrafish larval development. This will allow for a fast and unbiased evaluation of pathophysiological events during toxicological studies. To achieve this, the imaging process has to be optimized and a reliable model for sequence recognition based on deep learning has to be developed.
Geophysical Joint Inversion for Accurate Brain Myelin Mapping
The 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.
Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service
In order to detect impending landslides before they occur and to enable reliable emergency mapping after a landslide, the researchers are combining optical data with radar data from satellites. Using machine learning methods, computers will be trained to recognise the tiniest of changes in things like sloping landscape surfaces.