Abstract The choice of segmentation method for high-resolution peripheral quantitative computed tomography (HR-pQCT) scans influences accuracy of bone microarchitecture measurements. Smaller or under-mineralized bone can present challenges in accurate extraction of bone structure using global thresholding methods, as local variations in intensity are not considered and finer structural details are not detected. This is especially important in small hand bones where bone structure is finer, or younger populations where bone tissue may be under mineralized. This study compared accuracy of global thresholding methods using Gaussian and Laplace-Hamming filters, and an adaptive local thresholding (AT) method in HR-pQCT scans of carpal bones. Eight ex vivo human cadaveric forearms (n=64 carpal bones) were analyzed. Three specimens (n=24 carpal bones, 2 female, mean age: 82.7 ± 4.6 years) were used for AT parameter optimization, and five specimens (n=40 carpal bones, 3 female, mean age: 82.0 ± 6.4 years) were used to compare trabecular microarchitecture accuracy and spatial agreement relative to micro-computed tomography (μCT, 20μm isotropic resolution). μCT images were segmented using a Gaussian filter and Otsu’s method, and HR-pQCT images were segmented using Gaussian filtering and global thresholding, Laplace-Hamming filtering and global thresholding, and the AT method. Trabecular thickness (Tb.Th), separation (Tb.Sp), and bone volume fraction (Tb.BV/TV) accuracy were evaluated, and spatial agreement was assessed using Dice similarity coefficients (DSC), 95th percentile Hausdorff distances (HD95), and average symmetric surface distances (ASSD). The AT method yielded the smallest absolute and relative errors, and lowest bias across all trabecular parameters. Compared to the Gaussian and fixed threshold method, AT reduced mean absolute error by 36% for Tb.Th, 14% for Tb.Sp, and 15% for Tb.BV/TV, and achieved the highest spatial agreement with μCT (DSC=0.84, HD95=0.061mm, ASSD=0.018mm). These findings extend prior HR-pQCT segmentation validation studies to carpal bones and demonstrate that AT outperforms the standard and Laplace-Hamming methods.
Kuczynski et al. (Thu,) studied this question.