Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimer’s disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms.

中文翻译：

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从 3D 图像重建微血管网络骨架：真相是什么？

微血管网络的结构变化日益成为多种疾病（例如阿尔茨海默病、血管性痴呆和肿瘤生长）发病机制的标志。这推动了专用 3D 成像技术的开发，以及能够使用 3D 重建网络来模拟功能行为（例如血流或运输过程）的计算建模框架的创建。从成像数据中提取 3D 网络大致包括两个图像处理步骤：分割和骨架化。许多研究工作都致力于分割领域，并且有标准且广泛应用的方法来创建和评估由手动注释或自动算法产生的黄金标准或基本事实。