Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, Korea.
Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea.
J Orthop Surg Res. 2023 Jun 1;18(1):398. doi: 10.1186/s13018-023-03870-x.
In this study, we proposed establishing an automatic computer-assisted surgical planning approach based on average population models.
We built the average population models from humerus datasets using the Advanced Normalization Toolkits (ANTs) and Shapeworks. Experiments include (1) evaluation of the average population models before surgical planning and (2) validation of the average population models in the context of predicting clinical landmarks on the humerus from the new dataset that was not involved in the process of building the average population model. The evaluation experiment consists of explained variation and distance model. The validation experiment calculated the root-mean-square error (RMSE) between the expert-determined clinical ground truths and the landmarks transferred from the average population model to the new dataset. The evaluation results and validation results when using the templates built from ANTs were compared to when using the mean shape generated from Shapeworks.
The average population models predicted clinical locations on the new dataset with acceptable errors when compared to the ground truth determined by an expert. However, the templates built from ANTs present better accuracy in landmark prediction when compared to the mean shape built from the Shapeworks.
The average population model could be utilized to assist anatomical landmarks checking automatically and following surgical decisions for new patients who are not involved in the dataset used to generate the average population model.
在这项研究中，我们提出了一种基于平均人群模型的自动计算机辅助手术规划方法。
我们使用高级标准化工具包（ANTs）和 Shapeworks 从肱骨数据集构建平均人群模型。实验包括（1）在手术规划之前评估平均人群模型，（2）在不涉及构建平均人群模型的过程中，验证平均人群模型在预测肱骨上临床标志方面的新数据集的应用。评估实验包括解释性变异和距离模型。验证实验计算了专家确定的临床真实标志与从平均人群模型转移到新数据集的标志之间的均方根误差（RMSE）。当使用来自 ANTs 的模板与使用来自 Shapeworks 的平均形状生成的模板进行比较时，评估结果和验证结果。
与专家确定的地面实况相比，平均人群模型预测新数据集上的临床位置具有可接受的误差。然而，与从 Shapeworks 生成的平均形状相比，来自 ANTs 的模板在预测地标方面具有更好的准确性。
平均人群模型可用于协助自动检查解剖学标志，并为未参与生成平均人群模型的数据集的新患者做出手术决策。
