There is a growing interest in developing 3D microscopy for the exploration of thick biological tissues. Recently, 3D X-ray nano computerized tomography has proven to be a suitable technique for imaging the bone lacuno-canalicular network. This interconnected structure is hosting the osteocytes which play a major role in maintaining bone quality through remodeling processes. 3D images have the potential to reveal the architecture of cellular networks, but their quantitative analysis remains a challenge due to the density and complexity of nanometer sized structures and the need to handle and process large datasets, e.g. 20483 voxels corresponding to 32 GB per individual image in our case. In this work, we propose an efficient image processing approach for the segmentation of the network and the extraction of characteristic parameters describing the 3D structure. These parameters include the density of lacunae, the porosity of lacunae and canaliculi, and morphological features of lacunae (volume, surface area, lengths, anisotropy etc.). We also introduce additional parameters describing the local environment of each lacuna and its canaliculi. The method is applied to analyze eight human femoral cortical bone samples imaged by magnified X-ray phase nano tomography with a voxel size of 120 nm, which was found to be a good compromise to resolve canaliculi while keeping a sufficiently large field of view of 246 μm in 3D. The analysis was performed on a total of 2077 lacunae showing an average length, width and depth of 17.1μm x 9.2μm x 4.4μm, with an average number of 58.2 canaliculi per lacuna and a total lacuno-canalicular porosity of 1.12%. The reported descriptive parameters provide information on the 3D organization of the lacuno-canalicular network in human bones. This article is protected by copyright. All rights reserved.

中文翻译：

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从 3D X 射线相位纳米断层扫描图像量化骨腔隙网络

开发用于探索厚生物组织的 3D 显微镜越来越受到关注。最近，3D X 射线纳米计算机断层扫描已被证明是一种适用于骨腔隙神经网络成像的技术。这种相互连接的结构承载着骨细胞，骨细胞在通过重塑过程维持骨质量方面发挥着重要作用。3D 图像有可能揭示蜂窝网络的架构，但由于纳米尺寸结构的密度和复杂性以及需要处理和处理大型数据集，例如 20483 个体素，每个图像对应 32 GB，因此它们的定量分析仍然是一个挑战在我们的情况下。在这项工作中，我们提出了一种有效的图像处理方法，用于网络分割和描述 3D 结构的特征参数的提取。这些参数包括腔隙的密度、腔隙和小管的孔隙率以及腔隙的形态特征（体积、表面积、长度、各向异性等）。我们还介绍了描述每个腔隙及其小管的局部环境的附加参数。该方法用于分析通过放大 X 射线相位纳米断层扫描成像的 8 个人类股骨皮质骨样本，体素大小为 120 nm，发现这是解决小管的良好折衷，同时保持足够大的 246 3D 中的微米。对总共 2077 个空隙进行了分析，显示平均长度、宽度和深度为 17.1μm x 9.2μm x 4.4μm，每个腔隙平均有 58.2 个小管，腔隙总孔隙率为 1.12%。报告的描述性参数提供了关于人体骨骼中腔隙小管网络的 3D 组织的信息。本文受版权保护。版权所有。