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Modenese, L. and J.-B. Renault. "Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries." Journal of Biomechanics: 110186. (2021)
Abstract    View

The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1° (knee joint) to 11° (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.

The aim of this project is to automatically generate skeletal OpenSim models from three-dimensional bone models obtained from medical images using the STAPLE toolbox.
The automatically created models are compared against models manually created from the same anatomical datasets.

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This work is framed in a long-term plan aiming to advance the state of the art of anatomical modelling by automating the most challenging tasks associated with musculoskeletal modelling. The related projects suggested below are part of this same effort.