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Objective. Few methods exist to measure the progression of osteoarthritis (OA) or to identify people at high risk of developing OA. Striking radiographic changes include deformation of the femoral head and osteophyte growth, which are usually measured semiquantitatively following visual assessment. In this study, an active shape model (ASM) of the proximal femur was used to determine whether morphologic changes to the bone could be quantified and used as a marker of hip OA.Methods. One hundred ten subjects who had no signs of radiographic hip OA at baseline (Kellgren/ Lawrence [K/L] scores 0-1) were selected from the Rotterdam Study cohort of subjects ages >55 years. To measure the progression of OA, subjects were followed up with radiographic assessment after 6 years. At the 6-year followup, 55 subjects had established OA (K/L score 3), and in 12 of these OA subjects, the progression of the disease required a total hip replacement (THR). Age-and sex-matched control subjects had a K/L score of 0 at followup. Using the ASM, subjects were assessed for shape changes in the femoral head and neck before, during, and after the development of radiographic OA.Scores of shape variance, or mode scores, were assigned for 10 modes of variation in each subject, and differences in mode scores were determined.Results. During followup, significant changes in shape of the proximal femur occurred within the OA group from baseline to followup (P < 0.0001 for mode 1 and P ‫؍‬ 0.002 for mode 6) but not within the control group. At baseline (all subjects having K/L scores 0-1), there were significant differences in mode 6 between the OA group and the control group (P ‫؍‬ 0.020), and in modes 3 and 6 between the OA subjects who underwent THR and the remaining OA subjects (P ‫؍‬ 0.012 and P ‫؍‬ 0.019, respectively).Conclusion. Compared with traditional scoring methods, the ASM can be used more precisely to quantify the deforming effect of OA on the proximal femur and to identify, at an earlier stage of disease, those subjects at highest risk of developing radiographic OA or needing a THR. The ASM may therefore be useful as an imaging biomarker in the assessment of patients with hip OA.Osteoarthritis (OA) is one of the most common disorders in the elderly. It is estimated that by age 75 years, 85% of individuals show either clinical or radiologic evidence of OA (1). As OA of the hip progresses, changes in the shape of the femoral head develop, with flattened and irregular features becoming apparent. These changes can be observed on standard radiographs but are hard to quantify. The severity of OA is generally assessed using semiquantitative methods based on visual evaluation of a radiograph; for example, the Kellgren/ Lawrence (K/L) scoring method (2) is used to assess a Supported by the Dutch Arthritis Association.

Image segmentation methods for CT can influence the accuracy of bone morphometry calculations. A new automated segmentation method is introduced, and its performance is compared with standard segmentation methods. The new method can improve the results of in vivo CT, where the need to keep radiation dose low limits scan quality.Introduction: An important topic for CT analysis of bone samples is the segmentation of the original reconstructed grayscale data sets to separate bone from non-bone. Problems like noise, resolution limitations, and beam-hardening make this a nontrivial issue. Inappropriate segmentation methods will reduce the potential power of CT and may introduce bias in the architectural measurements, in particular, when new in vivo CT with its inherent limitations in scan quality is used. Here we introduce a new segmentation method using local thresholds and compare its performance to standard global segmentation methods. Material and Methods: The local threshold method was validated by comparing the result of the segmentation with histology. Furthermore, the effect of choosing this new method versus standard segmentation methods using global threshold values was investigated by studying the sensitivity of these methods to signal to noise ratio and resolution. Results: Using the new method on high-quality scans yielded accurate results and virtually no differences between histology and the segmented data sets could be observed. When prior knowledge about the volume fraction of the bone was available the global threshold also resulted in appropriate results. Degrading the scan quality had only minor effects on the performance of the new segmentation method. Although global segmentation methods were not sensitive to noise, it was not possible to segment both lower mineralized thin trabeculae and the higher mineralized cortex correctly with the same threshold value. Conclusion: At high resolutions, both the new local and conventional global segmentation methods gave near exact representations of the bone structure. When scanned samples are not homogenous (e.g., thick cortices and thin trabeculae) and when resolution is relatively low, the local segmentation method outperforms global methods. It maximizes the potential of in vivo CT by giving good structural representation without the need to use longer scanning times that would increase absorption of harmful X-ray radiation by the living tissue.