  Recent advances in 3D imaging technologies provide novel insights to
researchers and reveal finer and more detail of examined specimen, especially
in the biomedical domain, but also impose huge challenges regarding scalability
for automated analysis algorithms due to rapidly increasing dataset sizes. In
particular, existing research towards automated vessel network analysis does
not consider memory requirements of proposed algorithms and often generates a
large number of spurious branches for structures consisting of many voxels.
Additionally, very often these algorithms have further restrictions such as the
limitation to tree topologies or relying on the properties of specific image
modalities. We present a scalable pipeline (in terms of computational cost,
required main memory and robustness) that extracts an annotated abstract graph
representation from the foreground segmentation of vessel networks of arbitrary
topology and vessel shape. Only a single, dimensionless, a-priori determinable
parameter is required. By careful engineering of individual pipeline stages and
a novel iterative refinement scheme we are, for the first time, able to analyze
the topology of volumes of roughly 1TB on commodity hardware. An implementation
of the presented pipeline is publicly available in version 5.1 of the volume
rendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).
