Bio-Image Analysis

Group Leader: Robert Haase

Portrait Robert Haase

Our Research Mission

Life-sciences rely heavily on multi-dimensional, high spatio-temporal, multi-channel microscopical imaging of many samples under various conditions. Major technical challenges encompass manifold aspects of large-scale image data mining: economic image acquisition, well-organized image data storage, efficient image processing, reliable quantitative bio-image analysis, reproducible post-processing workflows, and sustainably sharing experience in all these aspects. Analogously, similar challenges exist on the simulation side where scientists develop physical models mimicking biological observations. While the amount of available research image data is steadily increasing, also more and more new algorithms and methods emerge. The research field “Bio-Image Analysis” is moving fast. New technologies like graphics processing units (GPUs), artificial intelligence and collaborative cloud-based image analysis software are staggeringly changing the way how we interact with image data. We approach these major challenges by building solid bridges between communities, especially image data scientists who develop new methods and experimental scientists who are eager to work with top-notch scientific bio-image analysis software.

Our goal is to enable both scientific communities, algorithm developers and wet-lab experimentalists, to concentrate on their core-missions. Therefore, we automate post-acquisition image processing in the cloud, on local high-performance-computing infrastructure and on personal laptops. We combine state-of-the-art smart microscopy approaches, universally accessible large-scale image data storage, distributed computing, GPU-acceleration and machine learning algorithms in software libraries with handy user-interfaces that make state-of-the-art bio-image analysis sustainable and accessible to everyone.

Our approach is based on three main concepts

  • Interdisciplinary collaboration. We are convinced that only diverse teams of scientists such as physicists, biologists, chemists, microscopists, computer scientists and image data scientists can solve the puzzles behind the Physics of Life together. We are strong team-players, and we grow by crossing borders between disciplines regularly. We customize image data analysis workflows together with our collaborators to get the most out of experimental data. We also optimize experimental setups to improve image quality with respect to best-possible scientific reasoning. Constant exchange, rapid prototyping, pair-programming and regular hackathons are our methods of choice for achieving scientific and technological goals in teams.
  • Knowledge exchange. By sharing all our knowledge and skills in image data science and data mining, we support the Physics of Life community to answer scientific questions efficiently. Therefore, we offer lectures, courses and ad-hoc consulting in image analysis and data science theory, and state-of-the-art software such as ImageJ/Fiji, QuPath, Icy, Scikit-Image, Python, NumPy, SciPy, Napari, OpenCL, Java, Maven, Omero, Knime, CellProfiler and more.
  • Method exploration, exploitation and tool development. We are well connected in the international developer community of image data analysis algorithms, methods and platforms. We support collaborators in open-source and open-science projects in order to gain early-access to new upcoming top-notch image data analysis tools. We assemble these tools together with our local collaborators into image data analysis workflows and tools optimally suited for untangling the Physics of Life.

Are you as excited as we are? We’re welcoming collaborators and will have open positions soon. Get in touch!