Automated MicroCT-based bone and articular cartilage analysis using iterative shape averaging and atlas-based registration.
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.
Newton MD, Junginger L, Maerz T. Automated MicroCT-based bone and articular cartilage analysis using iterative shape averaging and atlas-based registration. Bone. 2020 Aug;137:115417. doi: 10.1016/j.bone.2020.115417. Epub 2020 May 13. PMID: 32416288.