Our Projects

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!

Project overview

Decorative image, HI ImageTox
Image: Jonas Baumann, HIPS


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.

Decorative image, HI HighLine


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.

Decorative image, HI EMSIG
Image: Johannes Seiffarth, FZ Jülich


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.

Decorative image, HI DIPLO


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”.

Decorative image, HI Deep4OM
Image: Hailong He, Helmholtz Munich


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.

Decorative image, HI BENIGN


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.

Decorative image, HI AutoCoast


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.

Decorative image, HI AIOrganoid
Image: Xun Xu, Hereon


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.

Decorative image explaining WeMonitor
Image: WeMonitor


Satellite-based Earth observation to detect natural hazards

Satellite imagery makes it possible to detect spatio-temporal anomalies on the Earth’s surface, including natural hazards such as landslides, deforestation, or the emergence of large waste dump sites. This project aims to use artificial intelligence to detect these changes at an early stage and to be able to monitor their progress.

Image: Ehsan Faridi, IEK-13, Forschungszentrum Juelich GmbH


Autonomous image analysis to accelerate energy materials discovery and integration

Research into green materials for clean energy generation is moving at full speed – yet still requires a long time to complete. This project is working on an open source image processing application that uses artificial intelligence to drive the analysis and management of image data from experiments across the energy materials community.

Image: DLR


On the trail of the mystery of the laws of calving

Researchers still face a mystery when it comes to the laws by which glaciers calve. This project aims to use satellite imagery, artificial intelligence, mathematical optimisation and a new data processing pipeline to track the movements of glacier fronts in Antarctica to get closer to solving the mystery.

Image: Paul Kamm, Helmholtz-Zentrum Berlin


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.

Image: SmartPhase project partners


Fast, intelligent 3-D X-ray images for material examination

In order to be able to improve materials, it helps to take a look at their microstructures. This is because valuable information about their properties and behaviour can be found there – for example information about when which ageing processes begin. The aim of this project is to automate and accelerate access to this information with the help of a smart imaging technique.



Leveraging Cross-Domain Synergies for Efficient Machine Learning of Nanoscale Tomogram Segmentation

The aim is to develop an adaptable algorithm that can be used to perform different tasks in data and image analysis without needing to be trained with new, laboriously annotated images for each separate task.



Ultra Content Screening for Clinical Diagnostics and Deep Phenotyping

A method will be developed in which selected biomarkers in tumour and bone marrow cells from cancer patients will be examined and analysed automatically. The novel technology is based on ultra content screening technology, which allows detailed insights at the single cell level.