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首页> 外文期刊>BMC Bioinformatics >Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
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Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

机译：大容量数据集中任意血管网络的可扩展鲁棒图和特征提取

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摘要

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 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. We 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. We 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.
机译：3D成像技术的最新进展为研究人员提供了新的洞察力，揭示了较好的和更细节的被检查的标本，特别是在生物医学领域，也对自动化分析算法的可扩展性造成了巨大挑战，这是由于快速增加的数据集尺寸而导致的自动化分析算法。特别是，对自动血管网络分析的现有研究并不总是考虑所提出的算法的内存要求，并且通常为由许多体素组成的结构产生大量的虚假分支。另外，这些算法通常具有进一步的限制，例如对树拓扑的限制或依赖于特定图像模态的性质。我们提出了一种可伸缩的迭代管道（在计算成本，所需的主内存和稳健性方面），其从船舶网络的前景分割中提取了船舶网络的前景分段的注释抽象图表示。新颖的迭代细化过程由单无碱，先验A-Prafti确定参数控制。我们首次能够使用所提出的管道分析商品硬件大约1？TB的拓扑的拓扑。与现有技术相比，我们在表面噪声，血管形状偏差和各向异性分辨率方面展示了改进的鲁棒性。呈现的流水线的实现在卷渲染和处理引擎Voreen的5.1版中公开使用。

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