Computer Assisted Research and Development Group (L.V., F.C., and P.F.) and Department of Orthopaedics (C.G.), Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland.
J Bone Joint Surg Am. 2018 Apr 18;100(8):e50. doi: 10.2106/JBJS.17.00829.
In computer-assisted reconstructive surgeries, the contralateral anatomy is established as the best available reconstruction template. However, existing intra-individual bilateral differences or a pathological, contralateral humerus may limit the applicability of the method. The aim of the study was to evaluate whether a statistical shape model (SSM) has the potential to predict accurately the pretraumatic anatomy of the humerus from the posttraumatic condition.
Three-dimensional (3D) triangular surface models were extracted from the computed tomographic data of 100 paired cadaveric humeri without a pathological condition. An SSM was constructed, encoding the characteristic shape variations among the individuals. To predict the patient-specific anatomy of the proximal (or distal) part of the humerus with the SSM, we generated segments of the humerus of predefined length excluding the part to predict. The proximal and distal humeral prediction (p-HP and d-HP) errors, defined as the deviation of the predicted (bone) model from the original (bone) model, were evaluated. For comparison with the state-of-the-art technique, i.e., the contralateral registration method, we used the same segments of the humerus to evaluate whether the SSM or the contralateral anatomy yields a more accurate reconstruction template.
The p-HP error (mean and standard deviation, 3.8° ± 1.9°) using 85% of the distal end of the humerus to predict the proximal humeral anatomy was significantly smaller (p = 0.001) compared with the contralateral registration method. The difference between the d-HP error (mean, 5.5° ± 2.9°), using 85% of the proximal part of the humerus to predict the distal humeral anatomy, and the contralateral registration method was not significant (p = 0.61). The restoration of the humeral length was not significantly different between the SSM and the contralateral registration method.
SSMs accurately predict the patient-specific anatomy of the proximal and distal aspects of the humerus. The prediction errors of the SSM depend on the size of the healthy part of the humerus.
The prediction of the patient-specific anatomy of the humerus is of fundamental importance for computer-assisted reconstructive surgeries.
在计算机辅助重建手术中，对侧解剖结构被确立为最佳的重建模板。然而，现有的个体内双侧差异或病理性对侧肱骨可能会限制该方法的适用性。本研究的目的是评估统计形状模型（SSM）是否有潜力从创伤后条件准确预测肱骨的创伤前解剖结构。
从 100 对无病理条件的尸体肱骨的计算机断层扫描数据中提取三维（3D）三角面模型。构建 SSM 以编码个体之间的特征形状变化。为了使用 SSM 预测肱骨近端（或远端）的患者特定解剖结构，我们生成了预先定义长度的肱骨段，排除要预测的部分。评估近端和远端肱骨预测（p-HP 和 d-HP）误差，定义为预测（骨）模型与原始（骨）模型的偏差。为了与最先进的技术（即对侧配准方法）进行比较，我们使用相同的肱骨段来评估 SSM 或对侧解剖结构是否提供更准确的重建模板。
使用肱骨远端的 85%来预测肱骨近端解剖结构时，p-HP 误差（平均值±标准差，3.8°±1.9°）显著较小（p=0.001）与对侧配准方法相比。使用肱骨近端的 85%来预测肱骨远端解剖结构时，d-HP 误差（平均值，5.5°±2.9°）与对侧配准方法的差异无统计学意义（p=0.61）。SSM 和对侧配准方法在肱骨长度的恢复方面没有显著差异。
SSM 可以准确预测肱骨近端和远端的患者特定解剖结构。SSM 的预测误差取决于肱骨健康部分的大小。
预测肱骨的患者特定解剖结构对于计算机辅助重建手术至关重要。
