Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression.
Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression.
复制标题
CMF手术后的软组织变形的预测，并具有递增的内核脊回归。
DOI：
10.1016/j.compbiomed.2016.04.020
复制
发表时间：
2016-08-01
影响因子：
7.7
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Pan B;Zhang G;Xia JJ;Yuan P;Ip HH;He Q;Lee PK;Chow B;Zhou X
截骨术后面部软组织变形与相应的骨及软组织生物力学特性有关。然而，没有一种设计用于预测截骨术后软组织变形的方法结合了基于人群的统计数据。本研究的目的是建立一个统计模型来描述生物力学特性与截骨后软组织变形之间的关系。我们提出了一个增量核岭回归（IKRR）模型来实现这一目标。模型的输入是通过有限元方法（FEM）计算的生物力学信息。输出是从成对的术前和术后3D图像生成的软组织变形。该模型随着每个新患者的生物力学信息而递增地调整。因此，IKRR模型使我们能够通过使用生物力学和统计信息来预测新患者的潜在软组织变形。这两种类型的数据的集成对于精确模拟手术后软组织变化至关重要。所提出的方法进行了评价留一交叉验证，使用11例患者的数据。我们的模型的平均预测误差（0.9103 mm）低于一些最先进的算法。该模型有望成为预防颅颌面手术后面部变形风险的可靠方法。
Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient’s biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103 mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.