Framework for the Repair of Vasculature Graphs

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.

The points, radii and their connectivity determine the vascular geometry. Such a dataset is generated by applying skeletonization algorithms to segmented 3D images of microvasculature and stored in HDF5 binary files. 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. 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 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. 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 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 ideal candidate should have strong experience with programming and algorithms. Furthermore, a background in tensor algebra, differential geometry and graph theory would be a plus. The student will develop problem-solving and programming skills in a real world problem and have the opportunity to practice his/hers discipline to a work environment. Reference: https://core.ac.uk/download/pdf/12042994.pdf