人体肌肉骨骼韧带的自动建模--框架综述与模型质量评价
Automatic Modelling of Human Musculoskeletal Ligaments -- Framework Overview and Model Quality Evaluation
摘要：
结缔软组织的精确分割仍然是一个具有挑战性的任务，这阻碍了相应的生物力学计算的几何模型的生成。或者，人们可以预测韧带附着部位，然后根据解剖学知识和形态学研究近似形状。在这里，我们描述了一个相应的集成框架，用于人体肌肉骨骼韧带的自动建模。我们将统计形状建模与几何算法相结合，以自动识别插入部位，基于此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。对于前臂解剖结构的采用，根据先前工作中提出的方法的解剖预测定义统计模型中的韧带附着部位。为了进行评价，我们将生成的部位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5个前臂，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，用最先进的插入部位预测训练的统计模型并不总是可靠的。使用该模型，与采用尸体研究的已知插入位置相比，平均均方误差以及网格的Hausdorff距离增加了一个数量级以上。使用后者的平均均方误差为0.59 mm，平均Hausdorff距离小于7 mm，为完整的韧带。总之，所提出的用于从插入点生成韧带形状的方法似乎是可行的，但是用SSM检测插入部位太不准确。
英文摘要：
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.
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