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首页> 外文期刊>IEEE Transactions on Medical Imaging >Automated Quantitative Bone Analysis in In Vivo X-ray Micro-Computed Tomography
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Automated Quantitative Bone Analysis in In Vivo X-ray Micro-Computed Tomography

机译：体内X射线微计算机断层扫描中的自动定量骨分析

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摘要

Measurement and analysis of bone morphometry in 3D micro-computed tomography volumes using automated image processing and analysis improve the accuracy, consistency, reproducibility, and speed of preclinical osteological research studies. Automating segmentation and separation of individual bones in 3D microcomputed tomography volumes of murine models presents significant challenges considering partial volume effects and joints with thin spacing, i.e., 50 to 100 μm. In this paper, novel hybrid splitting filters are presented to overcome the challenge of automated bone separation. This is achieved by enhancing joint contrast using rotationally invariant second-derivative operators. These filters generate split components that seed marker-controlled watershed segmentation. In addition, these filters can be used to separate metaphysis and epiphysis in long bones, e.g., femur, and remove the metaphyseal growth plate from the detected bone mask in morphometric measurements. Moreover, for slice-by-slice stereological measurements of long bones, particularly curved bones, such as tibia, the accuracy of the analysis can be improved if the planar measurements are guided to follow the longitudinal direction of the bone. In this paper, an approach is presented for characterizing the bone medial axis using morphological thinning and centerline operations. Building upon the medial axis, a novel framework is presented to automatically guide stereological measurements of long bones and enhance measurement accuracy and consistency. These image processing and analysis approaches are combined in an automated streamlined software workflow and applied to a range of in vivo micro-computed tomography studies for validation.
机译：使用自动图像处理和分析功能在3D微型计算机断层扫描体积中进行骨形态测量的测量和分析可提高临床前骨科学研究的准确性，一致性，可重复性和速度。考虑到部分体积效应和间距较窄（即50至100μm）的关节，在鼠模型的3D微计算机断层扫描体积中自动分割和分离单个骨骼提出了重大挑战。在本文中，提出了新型混合分裂过滤器，以克服自动骨分离的挑战。这是通过使用旋转不变的二阶导数运算符增强关节对比度来实现的。这些过滤器生成分裂的成分，为标记控制的分水岭分割提供种子。另外，这些过滤器可用于分离长骨例如股骨中的干meta端和骨physi端，并在形态计量学测量中从检测到的骨面罩中去除干phy端生长板。此外，对于长骨，特别是弯曲骨（例如胫骨）的逐层立体测量，如果引导平面测量遵循骨骼的纵向方向，则可以提高分析的准确性。在本文中，提出了一种使用形态学细化和中心线操作来表征骨内侧轴的方法。以中轴为基础，提出了一种新颖的框架，可自动引导长骨的立体测量并增强测量准确性和一致性。这些图像处理和分析方法结合在自动化的简化软件工作流程中，并应用于一系列体内微计算机断层扫描研究以进行验证。

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