Collaborations — Ongoing
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 […]
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 […]