Furthermore, a background in tensor algebra, differential geometry and graph theory would be a plus. The ideal candidate should have strong experience with programming and algorithms. The output of the repair framework will be a fully connected network with a topology as close as possible to the biological one (see reference for an example repair method article). The repair process will connect the disconnected parts of the network and reconstruct the gaps that have been introduced from the artifacts in the reconstruction / skeletonization process. The aim of the project is the creation of a C++ framework for repairing vasculature skeletonized graphs using state-of-the-art algorithms from the literature. However, automatic reconstruction of vasculature graphs generates artifacts, such as gaps, discontinuities and in general disconnected components, which obstruct the application of functional models. The skeleton is primarily used for the structural reconstruction of the neuronal – glial – vascular architecture, thus an accurate and high quality representation of the experimental data is required. Such a dataset is generated by applying skeletonization algorithms to segmented 3D images of microvasculature and stored in HDF5 binary files. The points, radii and their connectivity determine the vascular geometry. A skeleton vasculature dataset is a graph comprised of nodes which have properties of position and radius, and edges that specify the connectivity of the former.
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