Projects

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HighLine: High image quality for lines in MRI: from roots to angiograms

The project HighLine has the objective to enhance 3D Magnetic Resonance (MR) images displaying line-like structures such as plant roots or vessels with modern deep learning methods.

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EMSIG - Event driven Microscopy for Smart mIcrofluidic sinGle-cell analysis

The EMSIG project aims to bring unsupervised event detection (ED) capabilities to microfluidic live-cell analysis, being able to detect predefined event triggers from live-cell microscopy data, classifying these triggers in event types and connect the results with a computerized control of the microscopic imaging components.

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SATOMI - Tackling the segmentation and tracking challenges of growing colonies and microbial diversity

The SATOMI platform is designed to expand the range of use cases and to accelerate the time-to-insight in the field of microbial single-cell analysis.

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FluoMap: Create global fluorescence maps from satellite data with ML

FluoMap pursues the goal of extracting fluorescence signals from the measurement data of the satellite- or airborne imaging spectrometers DESIS and HyPlant using state-of-the-art AI methods. This will make it possible to generate global fluorescence maps, which will play a key role in the worldwide monitoring of the vegetation health status.

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HDS-LEE: Helmholtz School for Data Science

Within the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), IAS-8 is involved via a PhD project on transfer learning methods for computational medicine.

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Last Modified: 10.09.2024