NOVA - Network for Online Visualization and synergistic Analysis of Tomographic Data
Aims & Objectives
Synchrotron X-ray microtomography offers unique opportunities for the morphological analysis of animals. Internal structures become observable even in opaque organisms in a non-invasive, three-dimensional way, and with a sub-micron resolution. The method thus allows for the investigation of objects from a wide field of biological research. Biomechanics, for example, is dealing with the complex dynamics of muscle-skeleton interactions, neurobiology with the topology of sensory centres in the brain, and developmental biology with differential comparisons of developmental stages.
So far, the enormous potential of collaborative work on the same specimens is mostly untapped. By using a new collaborative approach, this project aims to create new possibilities, that allow for a more efficient use of the valuable beam time at tomographic synchrotron beamlines through the coordination of research on different organ systems and a regulation of the data usage by a common data policy. The (associated) biological partners of the joint research project NOVA cover different scientific aspects of morphology and thus serve as a model community for the cooperative, integrative approach. As exemplification for the biological objects, complementary aspects of insect head morphology will be investigated.
The demand for cross-community chains of analyses necessitates several technological developments within the framework of this project. Within the ASTOR project the close cooperation of biologists and image processing experts has already successfully demonstrated considerable progress in semi-automatic segmentation and classification of internal structures. The EMCL and its partners tie up with this progress in the NOVA project and enhance the existing segmentation methods with regard to the following 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:
- Volumebased image segmentation and Uncertainty Quantification (UQ)
- Solving inverse problems
- Back-coupling the segmentation process with the exposure parameters
- The project is founded by the Federal Ministry of Education and Science of Germany BMBF.
- Technische Universität Darmstadt (TUD)
- Engineering Mathematics and Computing Lab (EMCL),
Interdisziplinares Zentrum für wissenschaftliches Rechnen, Universität Heidelberg (UHD)
- Karlsruher Institut für Technologie (KIT)
- Zentrum für Materialforschung und Küstenforschung, Helmholtz-Zentrum Geesthacht (HZG)
- Institut für Prozessdatenverarbeitung und Elektronik, Karlsruher Institut für Technologie (IPE)
- ANKA Synchrotronstrahlungsquelle (ANKA)
- Universität Tübingen
- Universität Jena
- University of Hull
- Universität Greifswald
People from EMCL
- van de Kamp T, Schwermann AH, dos Santos Rolo T, Lösel PD, Engler T, Etter W, Faragó T, Göttlicher J, Heuveline V, Kopmann A, Mähler B, Mörs T, Odar J, Rust J, Tan Jerome N, Vogelgesang M, Baumbach T, Krogmann L
Parasitoid biology preserved in mineralized fossils
Nature Communications , vol 9, Article number: 3325, doi:10.1038/s41467-018-05654-y, 2018.
- Schmelzle S, Heethoff M, Heuveline V, Lösel P, Becker J, Beckmann F, Schluenzen F, Hammel JU, Kopmann A, Mexner W, Vogelgesang M, Jerome NT, Betz O, Beutel R, Wipfler B, Blanke A, Harzsch S, Hörnig M, Baumbach T and van de Kamp T
The NOVA project: maximizing beam time efficiency through synergistic analyses of SRμCT data
Proc. SPIE 10391, Developments in X-Ray Tomography XI , 103910P, doi:10.1117/12.2275959, 2017.
- Lösel P and Heuveline V
A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation
in: Zuluaga M, Bhatia K, Kainz B, Moghari M and Pace D (eds) Reconstruction, Segmentation, and Analysis of Medical Images. Lecture Notes in Computer Science, vol 10129, Springer, Cham, doi:10.1007/978-3-319-52280-7_12, 2017.