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To generate subject-specific musculoskeletal models for clinical use, the location of muscle attachment sites needs to be estimated with accurate, fast and preferably automated tools. For this purpose, an automatic method was used to estimate the muscle attachment sites of the lower extremity, based on the assumption of a relation between the bone geometry and the location of muscle attachment sites. The aim of this study was to evaluate the accuracy of this morphing based method. Two cadaver dissections were performed to measure the contours of 72 muscle attachment sites on the pelvis, femur, tibia and calcaneus. The geometry of the bones including the muscle attachment sites was morphed from one cadaver to the other and vice versa. For 69% of the muscle attachment sites, the mean distance between the measured and morphed muscle attachment sites was smaller than 15 mm. Furthermore, the muscle attachment sites that had relatively large distances had shown low sensitivity to these deviations. Therefore, this morphing based method is a promising tool for estimating subject-specific muscle attachment sites in the lower extremity in a fast and automated manner. [ABSTRACT FROM AUTHOR]

For individualization of a biomechanical model, it is necessary to estimate the muscle attachments of the person to whom it is to be adapted. One of the methods to estimate muscle attachments is to use model transformations to transform a model with known muscle attachments to the bones of a person. We hypothesize that the location and shape of muscle attachment sites correlate with the shape of the bones they are attached to. If this hypothesis holds, it is possible to predict the location of muscle attachments when the shape of the bones is known. To validate this hypothesis, geometric models of three sets of shoulder bones were built. These models consist of 3-D surface models of the scapula, clavicle, and humerus, with the muscle attachment contours connected to them. By means of geometric transformations, the models were transformed, so the muscle attachments of the different data sets could be compared. Using these techniques, 50 per cent of the muscle attachment contours could be predicted with high accuracy. The muscle attachment contours that could not be predicted were all influenced by measurement errors. For 30 per cent of the muscle attachment contours, it was not possible to distinguish the interindividual differences from the inaccuracies of the method used. From this study, we concluded that most muscle attachment contours can be predicted by means of geometric models of the bones. [Copyright &y& Elsevier]