Thethree-dimensional (3D) imaging and quantitative analysis of bone microvasculature are important to describe angiogenesis involvement in bone metastatic processes. Here, we propose an algorithm based on marker-controlled watershed for the 3D segmentation of vessels and bone in mouse bone imaged with a contrast agent using synchrotron radiation micro-computed tomography (SR-μCT). Markers were generated using hysteresis thresholding and morphological filters, and the control surface was constructed using the monogenic signal phase asymmetry. The accuracy and robustness of the proposed method were evaluated on a series of synthetic volumes generated to mimic the vessel, bone and background structures. Different contrast between different structures, as well as different noise levels were considered. A series of multi-class synthetic volumes were segmented using the proposed method, and the overall segmentation quality was evaluated using the Matthews correlation coefficient (MCC) by comparing to the ground truth. Additionally, we evaluated the segmentation of thin structures under various levels of Gaussian noise. The simulation study indicated that the algorithm was performant in multi-class segmentation with different contrast, noise, and thickness. The algorithm was applied to images of bone from a mouse model of breast cancer bone metastasis acquired using SR-μCT. The segmentation quality was evaluated using the Dice coefficient and the MCC by comparing to manual segmentation. The proposed method performed better than hysteresis thresholding and marker-controlled watershed using the magnitude of the gradient as control surface. Several quantitative parameters on bone and vessels were extracted, including bone volume fraction (BV/TV), vessel volume fraction (VV/TV) and the mean vessel thickness (VTh). The bone volume fraction (BV/TV) was significantly lower in the metastatic group compared to the healthy group. This demonstrated the effectiveness of the algorithm for the study of bone and vessel microstructures in mouse model.

骨微血管系统的三维 (3D) 成像和定量分析对于描述骨转移过程中的血管生成参与很重要。在这里，我们提出了一种基于标记控制分水岭的算法，用于使用同步辐射微计算机断层扫描 (SR- μCT）。使用滞后阈值和形态滤波器生成标记，并使用单基因信号相位不对称构建控制面。在模拟血管、骨骼和背景结构的一系列合成体积上评估了所提出方法的准确性和稳健性。考虑了不同结构之间的不同对比度以及不同的噪声水平。使用所提出的方法对一系列多类合成卷进行分割，并通过与地面实况比较，使用马修斯相关系数（MCC）评估整体分割质量。此外，我们评估了不同级别的高斯噪声下薄结构的分割。仿真研究表明，该算法在具有不同对比度、噪声和厚度的多类分割中具有良好的性能。该算法应用于使用 SR- 获得的乳腺癌骨转移小鼠模型的骨骼图像。μCT。通过与手动分割进行比较，使用 Dice 系数和 MCC 评估分割质量。所提出的方法比使用梯度大小作为控制面的滞后阈值和标记控制的分水岭表现得更好。提取了骨和血管的几个定量参数，包括骨体积分数 (BV/TV)、血管体积分数 (VV/TV) 和平均血管厚度 (VTh)。与健康组相比，转移组的骨体积分数 (BV/TV) 显着降低。这证明了该算法在小鼠模型中研究骨骼和血管微结构的有效性。