用于膝关节软骨生物力学建模的带自动几何滤波的Swin UNETR分割方法
Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage
AI总结：
本研究提出结合3D Swin UNETR自动分割与自动几何滤波的方法，用于膝关节软骨分割，可高效构建高精度的受试者特异性膝关节有限元模型，力学性能与手动分割模型无显著差异。
摘要：
有限元（FE）建模等仿真研究可揭示膝关节生物力学机制，这类机制若无需患者直接参与，通过实验方法可能无法获取。通用有限元模型虽已用于预测组织生物力学特性，但忽略了特定人群的几何形态、载荷和材料属性的差异。相比之下，受试者特异性模型考虑了这些因素，预测精度更高，但构建需要投入大量精力和时间。本研究旨在通过整合基于3D Swin UNETR的自动软骨分割算法，推进受试者特异性膝关节有限元建模的便捷化。该算法先对膝关节软骨进行初始分割，再通过自动几何滤波优化表面粗糙度和连续性。除了戴斯相似系数（DSC）、豪斯多夫距离等图像分割性能的标准指标外，本研究还通过有限元仿真评估了该方法的有效性。研究构建了9对分别采用手动分割和自动分割方法的膝关节软骨有限元模型，对比步态过程中预测的应力和应变响应。自动分割方法在股骨软骨上取得了89.4%的高戴斯相似系数，胫骨软骨为85.1%，自动分割与手动分割结果的豪斯多夫距离为2.3 mm。最大主应力与应变、流体压力、纤维应变、接触面积等力学结果显示，手动与自动有限元模型之间无显著差异。这些结果证明了所提出的自动分割方法在构建精准膝关节有限元模型方面的有效性。
英文摘要：
Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models.
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