Prof. Dr. Peter Sanders, Karlsruher Institut für Technologie
Prof. Dr. Ralf Mikut, Karlsruher Institut für Technologie
The project will develop methods for processing time series of high resolution 3D images. The goal is to obtain very high performance both with respect to result quality and computational efficiency on modern hardware.
We will demonstrate the performance of our methods using large real world inputs of developing zebrafish embryos generated by light-sheet microscopy. These datasets reach more than 10 Terabytes per embryo and will be two orders of magnitude larger than previously reported tools can handle. Large realistic, simulated inputs including ground truth will be used to quantitatively assess result quality.
An exemplary application domain is tracking of objects (e.g., cell nuclei, cytoplasm, nanoparticles) in microscopic images where different object classes are labeled with particular fluorescent dyes. These tracking problems are highly important in biological research and challenging because the known computational approaches can only scratch the surface of the wealth of information available in the huge datasets currently being acquired. Our strategy is to use high performance (e.g., graph based) algorithms for 3D image processing to extract objects annotated with uncertainty information. Since these results in great reduction of data volumes, tracking algorithms can then afford to take a global view integrating uncertainty information into a consistent overall result.