Statistical shape model (SSM) black South African faces based on from facial landmarks on a CT and CBCT scan population

The research developed normative reference values of black South African faces for various inter-landmark distances, and derived a statistical shape model (SSM) of 3D facial shape variation which can be applied to estimate missing soft tissue segments on simulated defective faces, such as for facial prosthetics. The research was conducted on 235 computed tomography and cone beam computed tomography scans.1) SSM_BlackSA: The statistical shape model containing the shape variation of a black South African adult population is provided as a matlab output file2) Defect estimation accuracy: an excel file containing the root mean square errors for estimating defect reconstruction accuracy for various facial defects, including for the full nose, partial nose, orbit, cheek, lips, bi-ortibal and two types of composite defects (small and large)3) Intra and interobserver distances: this excel provides data of 2 observers that each manually calculated inter-landmark distances for 20 clinically relevant distances on the face. Each observer had 3 sets of landmarks.4) Landmark_coordinates: This excel supplies the x,y,z coordinates of 24 facial landmarks of 235 sample faces.5) Manual_interlandmark_distances: this excel gives the calculated inter-landmark measurements of 20 clinically relevant distances calculated from manually placed landmarks on the face. This information was used to determine the population norms.6) Samples_list: an excel with the samples used in this research with basic demographic information such as sex, age and modality7) SSM_Graphic: a figure showing the visual representation of the SSM provided as a matlab file. This figure indicates the modes of variation most responsible for variation, as well as the multinormal distribution of variation

本研究致力于构建黑色南非人群的面部各关键点间距离的规范性参考值，并推导出一种三维面部形状变化统计形状模型（SSM），该模型可应用于模拟缺陷面部（如面部假体）中缺失软组织段的估计。研究基于235次计算机断层扫描和锥形束计算机断层扫描数据。1) SSM_BlackSA：包含黑色南非成年人群形状变化的统计形状模型以MATLAB输出文件形式提供。2) 缺陷估计精度：一个Excel文件，包含估计各种面部缺陷（包括整个鼻子、部分鼻子、眼眶、脸颊、嘴唇、双颞部和两种复合缺陷（小和大）的缺陷重建精度的均方根误差。3) 观察者间距离和观察者内距离：此Excel文件提供了两位观察者手动计算面部20个临床相关距离的关键点间距离的数据。每位观察者均有三套关键点。4) 关键点坐标：此Excel文件提供了235个样本面部24个面部关键点的x、y、z坐标。5) 手动关键点间距离：此Excel文件提供了从面部手动放置的关键点计算出的20个临床相关距离的关键点间测量值。这些信息用于确定人群规范。6) 样本列表：一个Excel文件，列出了研究中使用的样本及其基本人口统计学信息，如性别、年龄和方式。7) SSM_Graphic：一幅展示由MATLAB文件提供的SSM视觉表示的图像。此图像指出了导致变化的变异模式，以及变异的多正态分布。