提出新方法精准定位脊椎韧带附着点，提升生物力学建模可靠性。
SLD: Segmentation-Based Landmark Detection for Spinal Ligaments
- 先分割脊椎形状，再用领域规则识别不同附着点
- 在两个数据集上误差均低于1.1毫米，精度显著提升
- 适用于全脊柱区域，适合医学建模与临床研究
在生物力学建模中，韧带附着点的准确表示对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韧带标志点的向量，因此精确识别这些标志点是构建可靠脊柱模型的关键。现有自动化检测方法或局限于特定脊柱区域，或精度不足。本文提出一种新型脊椎韧带标志点检测方法：首先对3D椎体进行基于形状的分割，随后应用领域专用规则识别不同类型的附着点。该方法在多个患者、两个独立脊柱数据集上验证，平均绝对误差（MAE）为0.7 mm，均方根误差（RMSE）为1.1 mm，优于现有方法，在全脊柱区域表现出强泛化能力。
原文摘要 · Abstract (English)
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.
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