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Big Data Analytics

The Big Data Analytics group develops software and methodology to build a microscopic resolution 3D model of the human brain, which includes detailed information about the spatial arrangement and the properties of neuronal cells and nerve fibers, and highly precise 3D maps of brain areas.

This requires microscopic imaging and analysis of large quantities of histological brain sections, leading to image datasets at the Petabyte scale.

At the intersection of neuroinformatics, computer vision and artificial intelligence (AI), our research addresses:

  1. Data and workflow management for high throughput microscopic imaging,
  2. Machine learning and computer vision algorithms for biomedical image analysis on high performance computers,
  3. Software development for structured remote access to large image datasets and interactive 3D exploration of high-resolution brain atlases over the web

We are embedded in an interdisciplinary network of partners. As part of the national AI community, we host the research group of Helmholtz AI for the research field “Information” in Jülich. We coordinate the development of brain atlas services for EBRAINS, an open European infrastructure for brain research that is co-developed by neuroscientists across Europe in the Human Brain Project (HBP). We work closely with neuroscientists and machine learning experts in Montréal, Canada, through our joint Helmholtz International BigBrain Analytics & Learning Laboratory (HIBALL), and maintain a close collaboration with colleagues at the Jülich Supercomputing Center (JSC), Heinrich-Heine University Düsseldorf, and RWTH Aachen.

Hochdurchsatz Mikroskopie

High throughput microscopy

We develop distributed data management workflows for handling large image datasets generated by high throughput microscopic imaging.

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Biomedizinische Bildanalyse

Biomedical image analysis

We develop machine learning and computer vision algorithms for processing and analyzing microscopic images.

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We develop software and metadata standards to make high-resolution brain models easily and openly accessible along the principles of FAIR data.

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