Application of automatic image segmentation to tibiae and vertebrae from ovariectomized rats

Automatic contextual segmentation algorithms were developed to objectively identify bone compartments in pQCT images of tibiae, femora, and vertebrae. Principal advantages of this approach over existing techniques such as histomorphometry are as follows: (a) the algorithms can be implemented in a fast, uniform, nonsubjective manner across many images, allowing unbiased comparisons of therapeutic efficacy; (b) much larger volumes in the region of interest can be analyzed to derive true volumetric parameters for trabecular and cortical bone compartments; and (c) pQCT can be used to quantitate bone effects longitudinally in vivo. An automatic contextual segmentation algorithm was used to analyze over 600 scans of proximal tibiae, distal femora, and L-4 vertebrae from studies with ovariectomized rats. Accuracy and precision analyses were performed, and correlation to histomorphometry parameters showed that pQCT trabecular bone density correlates to Tb.N with r = 0.93, while BV/TV correlates to Tb.N with r = 0.95. In other words, pQCT correlates as well to histomorphometry as histomorphometry does to itself. We conclude that the developed automatic segmentation algorithm provides fast, precise, and objective quantitation of bone compartments that are highly correlated with histomorphometry measurements.