Collaborations

Solving imaging challenges together.
Within one week of growth, these human neuronal cells extend neurites and form new interconnections, illustrating their capacity to establish complex networks and vulnerability to disturbance.
Image: Jo Nyffeler, Luisa Reger, Nicole Teßendorf, UFZ | Info

Helmholtz Imaging Collaborations are your opportunity to directly tap into the expertise of Helmholtz Imaging. As part of a Collaboration, you will team up with one or multiple of our units and closely work together on solving imaging-related problems. A Collaboration is of collaborative nature and necessitates the active contribution of all parties involved. Collaborations can run for up to six months or longer, allowing us to take a deep dive into a specific problem and solve it for good!

Suitable topics for a collaboration are anything that is related to imaging and image analysis. Our Helmholtz Imaging Units offer a wide range of expertise covering the entire imaging pipeline, from the development of new imaging modalities all the way to AI-based image analysis. Whether you require support in (AI-based) image analysis, image reconstruction, annotation, data management, software solutions, addressing inverse problems, developing novel imaging modalities, 3D visualization and more – we are your partner. For more information about the expertise of our units, take a look at our team page. 

Interested in a Collaboration? Apply now!

The application process is simple and straightforward: Applications are handled via the Support Hub. Either send us an email or use our application form.

Your application should include the following information:

  • Problem statement,
  • Desired outcome / how can HI help solve the problem,
  • List of participating researchers,
  • Contact information of main applicant,
  • Depending on the task additional information is considered beneficial (e.g. sample data, respective annotations, prior publications etc.),
  • (optional) If you have already been in contact with one of our units or this application has emerged from an interaction initiated by the Support Hub feel free to name a desired Helmholtz Imaging unit to collaborate with

Review of your application: We will then review your application and, unless a desired partner unit is specified, match you with one of our units. If your application is accepted, you and the designated Helmholtz Imaging unit will then enter the evaluation phase.

Evaluation phase (duration 1-2 weeks): The goal of the exploration phase is to assess the feasibility of the project, agree on a desired (quantifiable) outcome and set a preliminary timeline.

Working Phase (up to 6 months): Following a successful evaluation phase, you and your Helmholtz Imaging collaborators work on the actual project. 

If the time is up and the predefined goals are not yet reached, both partners assess the current state of the project and evaluate whether a prolongation is desired and may lead to the desired outcome. If both parties agree, the Collaboration can be extended.

Just like the rest of our portfolio, Helmholtz Imaging Collaborations are free of charge. Helmholtz Imaging Collaborations do not offer opportunities for obtaining additional funding. Any contribution of Helmholtz Imaging is subject to our Publication Policy.

Collaboration Gallery

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VoltRon is an R based spatial omic analysis toolbox designed for multi-omics integration through spatial image registration. Focused on image registration, VoltRon leverages OpenCV—fully embedded into the package using Rcpp—to detect common features across images, ensuring precise alignment of multiple spatially-aware data modalities.
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Image Registration for VolTron

VoltRon is an R based spatial omic analysis toolbox designed for multi-omics integration through spatial image registration. Focused on image registration, VoltRon leverages OpenCV – fully embedded into the package using Rcpp – to detect common features across images, ensuring precise alignment of multiple spatially-aware data modalities. The toolbox includes built-in mini Shiny apps that […]
3D image of BESSY II
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Capturing Reality at BESSY II

The experimental hall at BESSY II (Berliner Elektronenspeicherring) is a constantly evolving space, featuring a diverse range of structures, devices, and experiments managed by various departments, companies, and individuals. This dynamic nature necessitates regular updates to ensure the floor plan accurately reflects the hall’s current state. To address this, a video drone is deployed to […]
Visual; Collaboration on "AI-based classification of phytoplankton community composition from field samples"
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AI-based classification of phytoplankton community composition from field samples

Phytoplankton biodiversity is a key indicator of marine ecosystems health. Due to climate change derived impacts, phytoplankton communities worldwide are being affected by changes in the water temperatures and the access to nutrients and light. Current methods to quantitatively classify phytoplankton samples require taxonomy experts to manually count and classify the individual cells they see […]
Image: Andreas Müller, Deborah Schmidt, Martin Weigert. Blender rendering of organelles within a single beta cell

BetaSeg

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and […]
Visual, HI Collaboration MSPaCMAn
Image: Dr. Jose Ricardo da Assuncao Godinho, HZDR, Jan Philipp Albrecht, MDC | info

MSPaCMAn

MSPaCMAn is a workflow for quantifying mineral phases in 3D images of particulate materials using X-ray computed micro-tomography, designed to minimize imaging artifacts. It involves dispersing particles into samples, optimizing image processing to label individual particles, and identifying phases at the particle level by interpreting the grey-values of all voxels within each particle. This method […]
Snapshots taken from videos created by the tool - original data by Almudena Garcia-Garcia and Jian Peng of Umweltforschungszentrum (UFZ)
Image: Snapshots taken from videos created by the tool - original data by Almudena Garcia-Garcia and Jian Peng of Umweltforschungszentrum (UFZ) | info

EarthWatch

Earth-Watch introduces a Python package that visualizes spatiotemporal netCDF data on world maps by generating dynamic videos with PyVista. Designed to enhance the understanding and presentation of geospatial datasets over time, the tool allows users to overlay multiple observations, enabling comparative analyses and revealing correlations between different datasets. Additionally, it facilitates effective communication and presentations. By […]
Visual, HI Collaboration Polarity-Jam
Image: Olya Oppenheim, MDC, Emir Bora Akmeriç, MDC, Jan Philipp Albrecht, MDC, Wolfgang Giese, MDC | info

Polarity-JaM

Cell polarity involves the asymmetric distribution of cellular components, cell shape, and contacts with neighboring cells. Gradients and mechanical forces bias cell polarity, coordinated by communication between adjacent cells. Advances in fluorescence microscopy and deep learning for image segmentation have expanded our understanding of cell polarity in health and disease. Polarity-JaM is an open-source package […]
Decorative Image for HI Collaboration Identifying structural features of zebrafishes, using Semantic Segmentation
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Identifying structural features of zebrafishes, using Semantic Segmentation

Zebrafish have a certain genetic similarity to humans and vertebrates. Particularly, the use of embryos is attractive due to the small scale screening capacity. Furthermore, similar to genuine cellular in vitro approaches, zebrafish embryos are considered as alternatives to animal testing. Therefore, they play a fundamental role in the detection of environmental and human health […]
Decorative Image for HI Collaboration Automated Analysis of Evolutionary Experiments of Phytoplankton
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Automated Analysis of Evolutionary Experiments of Phytoplankton

There is a strong interest in understanding community assembly and dynamics. Experimental approaches using phytoplankton have proven to be extremely insightful to unravel underlying biological processes. Imaging flow cytometry is an emerging method becoming more and more popular in different fields. It allows us to capture changes within a community or population in a more […]
Decorative Image for HI Collaboration for biopores and plant roots in soil cores using semantic segmentation of CT images
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Understanding and analyzing biopores and plant roots in Soil Cores using Semantic Segmentation of CT Images

To understand how processes in ecosystems work and how they are connected the analysis of soil systems is essential. Since traditional computer vision methods for analysing soil cores reach their limits the next step is to integrate deep learning methods. Therefore a sufficient amount of labeled ground truth data is needed. Since labeling this large […]
Pick Yolo
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Pick Yolo

In collaboration with the Center for Structural Systems biology, Helmholtz Imaging has developed and trained a convolutional neural network for the picking of instances of proteins in cryoelectrontomograms (CryoET). The so picked instances are subsequently used to reconstruct a 3D structure of the proteins using subtomogram averaging methods. The novel picking methods exploit the well […]
 

Plaque Assays

In collaboration with the Leibniz Institute of Virology and the Center for Structural Systems biology, Helmholtz Imaging is working on a processing pipeline for plaque assays. Plaque Assays are used to e.g. estimate the virus concentration in a given sample. For this high resolution images of cell cultures which have been infected with a virus […]
 

Segmentation of biodegradable bone implants

Together with the experts from Hereon the Helmholtz Imaging Support Team collaborates on segmenting synchrotron CT data. In 2021 the collaboration on semantic segmentation of biodegradable bone implants using a U-net resulted in two publications acknowledging the contribution of Helmholtz Imaging [1],[2]. Beyond that, we studied “Instance segmentation of paper fibers” imaged at the Hereon […]
 

Unisef

In the context of the Unisef project funded by Helmholtz AI, we implemented the prototype of a webservice to allow training and application of deep learning networks for segmentation running on the HPC infrastructure of DESY. The main idea here is to make DL based segmentation accessible and usable by non-experts, as well as complementing […]
 

Active Learning-enabled Generation of (Patho-)Physiological Lung Architectures for Pulmonary Medicine (ALEGRA)

ALEGRA aims to discover new insights into the morphology, physiology, and functionality of mammalian lungs. Therefore, quantitative parameters will be extracted from light sheet fluorescence microscopy images featuring comprehensive annotations of unprecedented detail for structures of interest such as hollow airways, blood vessels, as well as alveoli. These annotations are made possible by a novel […]
 

Connecting membrane pores and production parameters via machine learning (COMPUTING)

Isoporous block-copolymer membranes play a fundamental role in the filtration of liquids and can, for example, be used to purify drinking water. Despite recent progress in understanding the membrane formation process, finding suitable production parameters for a given precursor material (such as polymers of a certain length) still occurs in a trial-and-error fashion, wasting materials, […]
 

Detecting Type-2 Diabetes in histopathological images for a better understanding of biological processes behind the disease

Type-2 diabetes is a chronic disease affecting about 500 million people worldwide. Despite extensive research over the last decades, exact biological processes leading to a deteriorating insulin production are not yet fully understood. By building models that are able to classify whether a patient has type-2 diabetes or not from whole slide images of the […]
 

Predicting Perovskite Thin-Film Photovoltaic Performance from Photoluminescence Videos

Photovoltaics are a key technology to decarbonize the generation of energy. While perovskite thin-films are a promising option to build powerful next generation photovoltaics demonstrating high power conversion efficiencies, their manufacturing process remains unstable. We build a model that directly predicts the solar cell performance based on a video capturing the perovskite layer formation prior […]
 

Single hit detection in single particle imaging with XFELs

Single particle imaging with x-ray-free electron lasers enables unique insights into the inner structure of nanometer-sized biological particles such as viruses. In order to reconstruct their 3D composition, a large number of 2D diffraction patterns must be acquired. AI-based isolation of single hits from among the hundreds of thousands of acquisitions made throughout the course […]
 

Segmenting cell membranes in cryoET data

In this project we develop semantic segmentation methods for accurate delineation of cell membranes in cryoET data. Not only do these segmentations enable the analysis of the spatial configuration of membranes, for example in chloroplasts, they also facilitate downstream analysis like determining the distribution of membrane proteins on their surface.
 

Optimizing electrode geometry and surface for hydrogen production

Electrolysis of water into oxygen and hydrogen is a cornerstone of modern energy storage, electric mobility and the transition towards a net-zero-emissions industry. Maximizing the efficiency of this technology is key to its economically viable wide scale adoption. One approach to both reduce the costs and improve the overall efficiency is to enhance the bubble […]
 

Extracting clinically relevant parameters from real-time MRI images of fontan hearts

Obtaining accurate segmentations of the heart in real-time MRI allows a more realistic view on clinically relevant parameters, such as the stroke volume. Cardiac real-time MRI can assess diastolic filling under breath maneuvers or other cardiac load situations which potentially enhances diagnostics other than CINE breath hold cardiac MRI. Real-time MRI allows rapid acquisitions during […]
 

Segmentation and identification of tooth instances in cone-beam CT scans

The three dimensional labeling and identification of teeth in cone-beam CT scans is a time-consuming and challenging process which is exacerbated by (partially) missing teeth, tooth misalignments as well as the presence of implants or other utilities like metal plates or wires. As part of this project we develop an AI-based instance segmentation algorithm to […]
 

Understanding and analyzing plant roots using semantic segmentation of MRI images

The optimization of plants has long focused on the above-ground parts. Recently, new efforts are being made to exploit the potential below ground. To this end, our partners at the FZJ have developed an imaging system which enables imaging the root system throughout the growth of the plants using MRI. Besides qualitative analysis, a precise […]
 

Paving the way for future mineral processing and recycling technologies through large-scale analysis of particulate samples

Understanding and quantifying the exact composition of mineral samples paves the way towards advanced methodologies that not only increase the effectiveness of ore processing but also enable future recycling technologies. To this end, samples consisting of ground particles embedded into an epoxy matrix are imaged with computed tomography (CT) at micrometer resolution. Due to the […]
 

Ultra Content Screening for Clinical Diagnostics and Deep Phenotyping

For the Helmholtz Imaging project UCS (Ultra Content Screening for Clinical Diagnostics and Deep Phenotyping) we developed software to allow the project partners to process their immunofluorescence labelled light microscopy data of nuclei and cells for single cell proteomics. The developed pipeline includes instance segmentation of nuclei and cells in the immunofluorescence assays, registration of […]
 

Understanding lung diseases and optimizing their treatment

Understanding the distribution of nanoparticles after inhalation can lead to the discovery of novel, more effective drug delivering techniques. To this end we develop AI-based methods for semantic segmentation of the airways in non-dissected whole murine lungs, imaged with light sheet fluorescence microscopy. The analysis of airway properties in diseased lungs will furthermore shed light […]