Projects


With our Helmholtz Imaging Projects, Helmholtz Foundation Model Initiative (HFMI) and third-party funded projects, we aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.

Helmholtz Imaging offers a funding line of Helmholtz Imaging Projects, striving to seed collaborations between centers and across research fields. They are a strong incentive to enable interdisciplinary collaboration across the Helmholtz Association and an incubator and accelerator of the Helmholtz Imaging network. 

In addition to our Helmholtz Imaging Projects, the Helmholtz Imaging team has secured external funding for third-party projects contributing their knowledge and expertise on cutting-edge imaging methodology. 

Join us in unlocking the limitless potential of Helmholtz Imaging!

The next call for Helmholtz Imaging Projects is OPEN until July 30, 2025. Find out more about the project call in this summary.

Helmholtz Imaging Projects


 

SyNaToSe

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.
 

AsoftXm

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 […]
Visual to illustrate HI Project BrainShapes
 

BrainShapes

Laplace-Beltrami shape descriptors of brain structures: Comparative optimization and genetic dissection

The project explores the 3D structure of the human brain by creating a digital 'map' of the brain and examining its unique genetic properties, potentially linking genetic variations to brain disorders.

Helmholtz Foundation Model Initiative (HFMI) Projects


Microcosmos of the Ocean by Klas Ove Möller, Hereon
Image: NicoElNino on Shutterstock

AqQua

AqQua aims to build the first foundational pelagic imaging model using billions of aquatic images worldwide. These images, spanning species from plankton, will help an AI classify species, extract traits, and estimate carbon content, offering key insights into biodiversity, ecosystem health, and the biological carbon pump's role in climate regulation.
decorative image
Image: NicoElNino on Shutterstock

Synergy Unit

The Synergy Unit amplifies the Helmholtz Foundation Model Initiative's impact by developing AI principles for diverse fields. Collaborating with HFMI projects, it focuses on knowledge sharing, community building, and representation to ensure the initiative's lasting influence.
Image: NicoElNino on Shutterstock

The Human Radiome Project (THRP)

The Human Radiome Project (THRP) aims to drive a paradigm shift in medical research, providing novel insights into human health and disease through the power of AI. By integrating diverse radiological data, it seeks to enable groundbreaking advancements in personalized medicine, enhancing diagnostic accuracy and improving patient care.

Third-Party Projects


Visual for BestMeta
 

BestMeta

Behavioral Standard Metadata

Developing metadata standards and FAIR analysis pipelines for Video Tracking Assays (VTAs) in toxicology and medical sciences
Decorative image
 

Foundations of Supervised Deep Learning for Inverse Problems

Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.
visual for third-party funded project UMDISTO
 

UMDISTO: Unsupervised Model Discovery

The project aims to develop novel methods for unsupervised multi-matching to map cellular-level correspondences in organisms like C. elegans.