Orthopaedic Research Laboratories, Beaumont Health, Royal Oak, MI, United States of America.
Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, United States of America.
Bone. 2020 Aug;137:115417. doi: 10.1016/j.bone.2020.115417. Epub 2020 May 13.
Micro-computed tomography (μCT) and contrast-enhanced μCT are important tools for preclinical analysis of bone and articular cartilage (AC). Quantitative data from these modalities is highly dependent on the accuracy of tissue segmentations, which are often obtained via time-consuming manual contouring and are prone to inter- and intra-observer variability. Automated segmentation strategies could mitigate these issues, but few such approaches have been described in the context of μCT. Here, we validated a fully-automated strategy for bone and AC segmentation based on registration of an average tissue atlas. Femora from healthy and arthritic rats underwent μCT scanning, and epiphyseal trabecular bone and AC volumes were manually contoured by an expert. Average tissue atlases composed of 1, 3, 5, 10 and 20 pre-contoured training images (n = 10 atlases/group) were generated using iterative shape averaging and registered onto unknown images via affine and non-rigid registration. Atlas-based and expert-defined volumes for bone and AC were compared in terms of shape-based similarity metrics, as well as morphometric and densitometric parameters. Our results demonstrate that atlas-based registrations were capable of highly accurate and consistent segmentation. Atlases built from as few as 3 training images had no incidence of mal-registration and exhibited improved incidence of accurate registration, and higher sensitivity and specificity compared to atlases built from only one training image. Atlas-based segmentation of bone and AC from μCT images is a robust and accurate alternative to manual tissue segmentation, enabling faster, more consistent segmentation of pre-clinical datasets.
微计算机断层扫描（μCT）和对比增强 μCT 是骨和关节软骨（AC）临床前分析的重要工具。这些模态的定量数据高度依赖于组织分割的准确性，而组织分割通常通过耗时的手动轮廓获取，并且容易受到观察者内和观察者间的变异性的影响。自动化分割策略可以减轻这些问题，但在 μCT 背景下，很少有这样的方法被描述。在这里，我们验证了一种基于平均组织图谱配准的骨和 AC 全自动分割策略。来自健康和关节炎大鼠的股骨进行了 μCT 扫描，专家手动勾勒出骺板小梁骨和 AC 体积。使用迭代形状平均法生成了由 1、3、5、10 和 20 个预轮廓训练图像组成的平均组织图谱（每组 n = 10 个图谱），并通过仿射和非刚性配准将其注册到未知图像上。根据基于形状的相似性度量、形态计量学和密度计量学参数，比较了基于图谱和专家定义的骨和 AC 体积。我们的结果表明，基于图谱的配准能够实现高度准确和一致的分割。基于仅 3 个训练图像构建的图谱没有配准错误，并且与基于仅 1 个训练图像构建的图谱相比，具有更高的准确配准的发生率、更高的敏感性和特异性。基于 μCT 图像的骨和 AC 的图谱分割是手动组织分割的一种稳健且准确的替代方法，可实现更快、更一致的临床前数据集分割。
