Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014, Bern, Switzerland.
Med Eng Phys. 2010 Jul;32(6):638-44. doi: 10.1016/j.medengphy.2010.03.010.
Seventeen bones (sixteen cadaveric bones and one plastic bone) were used to validate a method for reconstructing a surface model of the proximal femur from 2D X-ray radiographs and a statistical shape model that was constructed from thirty training surface models. Unlike previously introduced validation studies, where surface-based distance errors were used to evaluate the reconstruction accuracy, here we propose to use errors measured based on clinically relevant morphometric parameters. For this purpose, a program was developed to robustly extract those morphometric parameters from the thirty training surface models (training population), from the seventeen surface models reconstructed from X-ray radiographs, and from the seventeen ground truth surface models obtained either by a CT-scan reconstruction method or by a laser-scan reconstruction method. A statistical analysis was then performed to classify the seventeen test bones into two categories: normal cases and outliers. This classification step depends on the measured parameters of the particular test bone. In case all parameters of a test bone were covered by the training population's parameter ranges, this bone is classified as normal bone, otherwise as outlier bone. Our experimental results showed that statistically there was no significant difference between the morphometric parameters extracted from the reconstructed surface models of the normal cases and those extracted from the reconstructed surface models of the outliers. Therefore, our statistical shape model based reconstruction technique can be used to reconstruct not only the surface model of a normal bone but also that of an outlier bone.
使用 17 块骨骼（16 块尸体骨骼和 1 块塑料骨骼）验证了一种从二维 X 射线射线图和从 30 个训练表面模型构建的统计形状模型重建股骨近端表面模型的方法。与以前介绍的验证研究不同，以前的研究使用基于表面的距离误差来评估重建精度，而这里我们建议使用基于临床相关形态参数测量的误差。为此，开发了一个程序，从 30 个训练表面模型（训练群体）、从 X 射线射线图重建的 17 个表面模型以及从 CT 扫描重建方法或激光扫描重建方法获得的 17 个地面真实表面模型中稳健地提取这些形态参数。然后进行了统计分析，将 17 个测试骨骼分为两类：正常情况和异常情况。这种分类步骤取决于特定测试骨骼的测量参数。如果测试骨骼的所有参数都在训练群体的参数范围内，则将该骨骼分类为正常骨骼，否则将其分类为异常骨骼。我们的实验结果表明，从正常情况下重建的表面模型中提取的形态参数和从异常情况下重建的表面模型中提取的形态参数之间在统计学上没有显著差异。因此，我们基于统计形状模型的重建技术不仅可以重建正常骨骼的表面模型，还可以重建异常骨骼的表面模型。
