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.

中文翻译：

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人体肌肉骨骼韧带的自动建模——框架概述和模型质量评估

结缔软组织的准确分割仍然是一项具有挑战性的任务，这阻碍了用于生物力学计算的相应几何模型的生成。或者，可以预测韧带插入部位，然后根据解剖知识和形态学研究近似形状。在这里，我们描述了用于人类肌肉骨骼韧带自动建模的相应集成框架。我们将统计形状建模与几何算法相结合，以自动识别插入位置，并根据创建几何表面和体积网格。为了演示临床用例，该框架已被应用于生成前臂骨间膜的模型。为通过前臂解剖学，根据先前工作中提出的方法根据解剖学预测定义统计模型中的韧带插入位点。为了进行评估，我们将生成的位点以及韧带形状与从尸体研究中获得的数据进行了比较，该研究涉及五个前臂，总共 15 条韧带。我们的框架允许创建具有良好保真度的近似韧带形状的 3D 模型。然而，我们发现用最先进的插入位点预测训练的统计模型并不总是可靠的。与使用尸体研究的已知插入位置相比，使用该模型，平均均方误差以及网格的 Hausdorff 距离增加了一个数量级以上。使用后者，平均均方误差为 0。对于完整的韧带组，产生了 59 毫米和小于 7 毫米的平均豪斯多夫距离。总之，所提出的从插入点生成韧带形状的方法似乎是可行的，但使用 SSM 检测插入位点太不准确。