With our Helmholtz Imaging Projects 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 interdisciplinarycollaboration across the Helmholtz Association and an incubator and accelerator of the Helmholtz Imaging network. 

In addition to our Helmholtz Imaging Projects, the Helmholtz Imaging te am 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 will be published in May 2024. Stay tuned!

Helmholtz Imaging Projects

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.


Neuroimaging Biomarkers for Restless Leg Syndrome

The 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.


Tackling the segmentation and tracking challenges of growing colonies and microbialdiversity

An artificial intelligence will observe the growth of bacteria: from microscope images of bacterial cultures taken at regular intervals, it will precisely track the development and division of individual cells – even when multiple bacterial species are cultivated together.

Third-Party Projects

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SFB Transregio 154 – C06: Transport metrics for analysis and optimization of network problems

SFB TRR 154 is a project of the German Research Foundation (DFG) and combines integer-continuous methods, model adaptation, and numerical simulation, to analyze and optimize gas markets, infrastructure, and control of networks. The third funding period specifically focuses on the transition from natural gas to hydrogen.
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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.
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Bayesian Computations for Large-scale (Nonlinear) Inverse Problems in Imaging

During research stays with the collaborating group at Caltech, we aim to investigate various aspects of statistical inverse problems. This includes inquiries into particle- and PDE-based sampling methods, as well as robust regularization using neural networks.