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Recent advances in 3D imaging technologies provide novel insights toresearchers and reveal finer and more detail of examined specimen, especiallyin the biomedical domain, but also impose huge challenges regarding scalabilityfor automated analysis algorithms due to rapidly increasing dataset sizes. Inparticular, existing research towards automated vessel network analysis doesnot consider memory requirements of proposed algorithms and often generates alarge number of spurious branches for structures consisting of many voxels.Additionally, very often these algorithms have further restrictions such as thelimitation to tree topologies or relying on the properties of specific imagemodalities. We present a scalable pipeline (in terms of computational cost,required main memory and robustness) that extracts an annotated abstract graphrepresentation from the foreground segmentation of vessel networks of arbitrarytopology and vessel shape. Only a single, dimensionless, a-priori determinableparameter is required. By careful engineering of individual pipeline stages anda novel iterative refinement scheme we are, for the first time, able to analyzethe topology of volumes of roughly 1TB on commodity hardware. An implementationof the presented pipeline is publicly available in version 5.1 of the volumerendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).

Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets