  Accurate segmentation of connective soft tissues is still a challenging task,
which hinders the generation of corresponding geometric models for
biomechanical computations. Alternatively, one could predict ligament insertion
sites and then approximate the shapes, based on anatomical knowledge and
morphological studies. Here, we describe a corresponding integrated framework
for the automatic modelling of human musculoskeletal ligaments. We combine
statistical shape modelling with geometric algorithms to automatically identify
insertion sites, based on which geometric surface and volume meshes are
created. For demonstrating a clinical use case, the framework has been applied
to generate models of the interosseous membrane in the forearm. For the
adoption to the forearm anatomy, ligament insertion sites in the statistical
model were defined according to anatomical predictions following an approach
proposed in prior work. For evaluation we compared the generated sites, as well
as the ligament shapes, to data obtained from a cadaveric study, involving five
forearms with a total of 15 ligaments. Our framework permitted the creation of
3D models approximating ligaments' shapes with good fidelity. However, we found
that the statistical model trained with the state-of-the-art prediction of the
insertion sites was not always reliable. Using that model, average mean square
errors as well as Hausdorff distances of the meshes increased by more than one
order of magnitude, as compared to employing the known insertion locations of
the cadaveric study. Using the latter an average mean square error of 0.59 mm
and an average Hausdorff distance of less than 7 mm resulted, for the complete
set of ligaments. In conclusion, the presented approach for generating ligament
shapes from insertion points appears to be feasible but the detection of the
insertion sites with a SSM is too inaccurate.
