In biomechanical modeling, the representation of ligament attachments is crucial for a realistic simulation of the forces acting between the vertebrae. These forces are typically modeled as vectors connecting ligament landmarks on adjacent vertebrae, making precise identification of these landmarks a key requirement for constructing reliable spine models. Existing automated detection methods are either limited to specific spinal regions or lack sufficient accuracy. This work presents a novel approach for detecting spinal ligament landmarks, which first performs shape-based segmentation of 3D vertebrae and subsequently applies domain-specific rules to identify different types of attachment points. The proposed method outperforms existing approaches by achieving high accuracy and demonstrating strong generalization across all spinal regions. Validation on two independent spinal datasets from multiple patients yielded a mean absolute error (MAE) of 0.7 mm and a root mean square error (RMSE) of 1.1 mm.

翻译：在生物力学建模中，韧带附着点的表征对于真实模拟椎体间作用力至关重要。这些力通常被建模为连接相邻椎体上韧带标志点的向量，因此精确识别这些标志点成为构建可靠脊柱模型的关键前提。现有自动化检测方法或局限于特定脊柱节段，或缺乏足够精度。本研究提出一种新颖的脊柱韧带标志点检测方法：首先对三维椎体进行基于形状的分割，随后应用特定领域规则识别不同类型的附着点。所提方法在实现高精度的同时展现出对所有脊柱节段的强泛化能力，性能优于现有方法。在两个独立的多患者脊柱数据集上进行验证，结果显示平均绝对误差（MAE）为0.7毫米，均方根误差（RMSE）为1.1毫米。