Department of Mechanical Engineering, University College London, United Kingdom.
Department of Mechanical Engineering, University College London, United Kingdom.
Comput Biol Med. 2024 Mar;171:108140. doi: 10.1016/j.compbiomed.2024.108140. Epub 2024 Feb 27.
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. The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super metric that compares the volume, connectivity, medialness, bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimise its parameters. Finally, we demonstrate that the super metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.
微血管网络的结构变化越来越多地被强调为广泛疾病发病机制的标志物，例如阿尔茨海默病、血管性痴呆和肿瘤生长。这促使人们开发了专门的 3D 成像技术，以及创建能够使用 3D 重建网络模拟血流或运输过程等功能行为的计算建模框架。从成像数据中提取 3D 网络通常包括两个图像处理步骤：分割和骨架化。分割领域已经投入了大量研究，并且有标准的、广泛应用的方法来创建和评估手动注释或自动算法生成的黄金标准或地面真实。然而，骨架化领域缺乏广泛应用的、简单的计算指标，用于验证或优化从二值图像中提取骨架的众多算法。这是一个特别严重的问题，因为 3D 成像数据集的规模不断增加，目视检查已不足以作为有效的验证方法。在这项工作中，我们首先通过将 4 种广泛使用的骨架化算法应用于 3 个不同的成像数据集，展示了这个问题的严重程度。在这样做的过程中，我们发现相同分割成像数据集的重建骨架之间存在显著的可变性。此外，我们还表明，这种结构可变性会传播到模拟度量，例如血流。为了减轻这种可变性，我们引入了一种新的、快速且易于计算的超级度量，该度量比较重建骨架的体积、连通性、中轴性、分支点识别和同源性与原始分割数据。然后，我们表明，该度量可用于选择给定数据集的最佳骨架化算法，以及优化其参数。最后，我们证明该超级度量还可用于快速识别特定骨架化算法的改进方向，成为理解网络中微小结构变化复杂影响的有力工具。
