Abstract
BackgroundRecent 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 always 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.ResultsWe propose a scalable iterative 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. The novel iterative refinement process is controlled by a single, dimensionless, a-priori determinable parameter.ConclusionsWe are able to, for the first time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen.
Highlights
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, and impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes
The study of vascular networks using modern 3D imaging techniques has become an increasingly popular topic of interest in biomedical research [2,3,4,5,6,7], as 2D slice analysis is limited to a small subset of the available data and cannot capture the 3D structure of vessel networks [8]
In this paper we present a pipeline designed to fulfill these requirements while extracting the topology, centerlines and edge associated features from binary volumetric images
Summary
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, and impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. Existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. In light of different imaging techniques, modalities and problem domains, the generation of a voxel-wise foreground segmentation can be seen as a sensible intermediate step for the automatic processing of vascular network images (see Fig. 1). The step of calculating topological, morphological or geometric features—a key requirement for the application in biomedical research and beyond—has not received sufficient attention from the research community [10], which mostly focused on developing novel methods [11,12,13] and software [14, 15] in the segmentation domain
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