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!
Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
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”.
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.
PlastoView
Microplastic Detection with the PlastiScope
Water quality is essential for ecosystems and human health, yet it's increasingly threatened by microplastics. This project develops image-based methods for detecting both plankton and microplastics using a new low-cost, mobile system.Helmholtz Foundation Model Initiative (HFMI) Projects
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.
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.
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.UNLOCK – Benchmarking Projects
RenewBench – A Global Benchmark for Renewable Energy Generation
Renewable energy’s variability makes grid management complex. RenewBench aims to provide standardized, high-quality data to advance trustworthy AI models and accelerate the transition to sustainable energy.
ForestUNLOCK: A multi-modal Multiscale Benchmark Dataset for AI-Driven Boreal Forest Monitoring and Carbon Accounting
Building the first consistent multi-modal single tree benchmark for forest structure and carbon stock assessments of the northern boreal forest
UQOB – Uncertainty Quantification in Object-detection Benchmark
Creating a benchmark dataset for object-detection and Uncertainty Quantification (UQ) in a multi-rater setting, to address annotation variability and AI model evaluation.Third-Party Projects
GLAM: Generative lung architecture modeling
This project is developing generative methods for designing bio-printable lung tissues across a spectrum of disease severity in the specific context of mouse and human lung disease.
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.