The geometrical representation of muscles in computational models of the musculoskeletal system typically consists of a series of line segments. These muscle anatomies are based on measurements from a limited number of cadaveric studies that recently have been used as atlases for creating subject-specific models from medical images, so potentially restricting the options for personalisation and assessment of muscle geometrical models. To overcome this methodological limitation, we propose a novel, completely automated technique that, from a surface geometry of a skeletal muscle and its attachment areas, can generate an arbitrary number of lines of action (fibres) composed by a user-defined number of straight-line segments. These fibres can be included in standard musculoskeletal models and used in biomechanical simulations. This methodology was applied to the surfaces of four muscles surrounding the hip joint (iliacus, psoas, gluteus maximus and gluteus medius), segmented on magnetic resonance imaging scans from a cadaveric dataset, for which highly discretised muscle representations were created and used to simulate functional tasks. The fibres’ moment arms were validated against measurements and models of the same muscles from the literature with promising outcomes. The proposed approach is expected to improve the anatomical representation of skeletal muscles in personalised biomechanical models and finite element applications.

在肌肉骨骼系统的计算模型中，肌肉的几何表示通常由一系列线段组成。这些肌肉解剖结构基于有限的尸体研究的测量结果，这些研究最近已用作地图集，用于根据医学图像创建特定于受试者的模型，因此可能限制了个性化和评估肌肉几何模型的选项。为了克服这种方法上的局限性，我们提出了一种新颖的，完全自动化的技术，该技术从骨骼肌的表面几何形状及其附着区域可以生成由用户定义的笔直直线组成的任意数量的作用线（纤维）。线段。这些纤维可以包含在标准的骨骼肌肉模型中，并用于生物力学模拟。该方法应用于髋关节周围四块肌肉（ilia骨，腰大肌，臀大肌和臀中肌）的表面，在来自尸体数据集的磁共振成像扫描上进行分割，为其创建高度离散的肌肉表示并用于模拟功能任务。根据文献中相同肌肉的测量和模型对纤维的弯矩臂进行了验证，结果令人鼓舞。所提出的方法有望改善个性化生物力学模型和有限元应用中骨骼肌的解剖学表现。为此，创建了高度离散的肌肉表示并将其用于模拟功能性任务。根据文献中相同肌肉的测量和模型对纤维的弯矩臂进行了验证，结果令人鼓舞。所提出的方法有望改善个性化生物力学模型和有限元应用中骨骼肌的解剖学表现。为此，创建了高度离散的肌肉表示并将其用于模拟功能性任务。根据文献中相同肌肉的测量和模型对纤维的弯矩臂进行了验证，结果令人鼓舞。所提出的方法有望改善个性化生物力学模型和有限元应用中骨骼肌的解剖学表现。