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ASTOR - Arthropod Structure revealed by ultrafast Tomography and Online Reconstruction

Aims & Objectives

X-ray imaging and tomography provide the opportunity to visualize internal structures of optically dense materials in 3D. Specifically, the invention of synchrotron-X-ray-microtomography was the onset of a new era of morphological research on microscopically small animals (e.g. micro-arthropods). Analyzing such 3D-data is time consuming and technically challenging. Especially the automation of classification and segmentation processes needs a close cooperation of biologists and image processing experts. Therefore, a new high-speed-tomography setup (UFO - Ultra-fast X-ray Imaging) is built at the ANKA synchrotron in Karlsruhe (KIT). This setup will enable unrivaled opportunities of high-throughput measurements and 3D/4D-tomographic imaging of dynamic systems and living organisms. However, it results in such large amounts of data that technical limitations will be reached regarding data acquisition, storage, organization and analyses. Hence, a strong integration of knowledge from biology, image processing and data management is needed to enable this new and fascinating high-speed-option for regular users in the future. Using the most speciose animal group on earth (arthropods) as a model system, the network for functional morphology and systematics aims to establish and standardize the synchrotron-X-ray-microtomography (SRµCT) to meet the needs of a broad range of biological research. Primary goals of the ASTOR project:

  • High-speed-tomography of dynamic systems and living organisms
  • Semi-automatic segmentation of large computed tomography datasets in 3D and 4D
  • Creation of an online-portal for morphological analyses based on cloud technologies

Research Topics

The EMCL provides the knowledge of HPC methodologies and the corresponding HPC infrastructure setup to challenge the tasks of semi-automatic segmentation of large computed tomography datasets in 3D and 4D. We develop and implement efficient numerical methods based on the application of new computer architectures like Multi-Core and Multi-GPU. A key issue lies in modelling and implementing the human visual thinking and its spatial visualization ability on a machine. Numerical modelling of semi-automatic segmentation algorithms requires both, the choice of an appropriate model and an efficient methodology for the solution process. The research is focused on the following topics:

  • Active Contours and the Level-Set Method
  • Partial Differential Equations and Random Walks
  • Numerical modelling of imaginative power
  • Highly-optimized numerical methods
  • High performance computing and optimization on parallel platforms
  • Providing image segmentation algorithms via REST-interfaces as a service

Funding

  • The project is founded by the Federal Ministry of Education and Science of Germany BMBF.

Partners

  • Ecological Networks, Technische Universität Darmstadt (TUD)
  • Interdisziplinares Zentrum für wissenschaftliches Rechnen, Universität Heidelberg (UHD)
  • Steinbuch Centre for Computing, Karlsruher Institut für Technologie (KIT)
  • ANKA Synchrotronstrahlungsquelle (ANKA)
  • Institut für Photonenforschung und Synchrontronstrahlung, Karlsruher Institut für Technologie (IPS)
  • Institut für Prozessdatenverarbeitung und Elektronik, Karlsruher Institut für Technologie (IPE)

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