Zuse Institute Berlin, Berlin, Germany.
1000 Shapes GmbH, Berlin, Germany.
Adv Exp Med Biol. 2019;1156:67-84. doi: 10.1007/978-3-030-19385-0_5.
In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression.Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated.We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) Statistical Shape Models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods or machine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring.
在我们的章节中，我们描述了如何从医学图像数据中重建三维解剖结构，以及如何从许多这样的重建中构建统计 3D 形状模型，从而产生一种新的解剖结构，不仅允许对解剖结构的变化进行定量分析，还可以进行视觉探索和教育可视化。未来的数字解剖图谱不仅将显示静态（平均）解剖结构，还将显示其在三维甚至四维中的正常或病理变化，从而说明生长和/或疾病进展。统计形状模型（SSM）是一种几何模型，以非常紧凑的方式描述语义相似的物体集合。SSM 表示许多三维物体的平均形状及其形状变化。SSM 的创建需要对应映射，可以通过相应的采样参数化来实现。如果可以为所有形状建立相应的参数化，则可以对个体形状特征之间的变化进行数学研究。我们将解释什么是统计形状模型以及如何构建它们。为铰接式耦合结构提供统计形状模型的扩展。除了形状外，物体的外观也将被集成到概念中。外观是一种独立于形状的视觉特征，取决于观察者或成像技术。典型的外观例如是在特定照明条件下物体的视觉表面的颜色和强度，或者使用计算机断层扫描（CT）或磁共振成像（MRI）进行的材料特性测量。（铰接式）统计形状模型与外观的统计模型的组合导致铰接式统计形状和外观模型（a-SSAMs）。在给出了各种用于人体器官、骨骼结构、面部和身体的 SSM 示例之后，我们将简要描述已经成功应用此类模型的临床应用。统计形状模型是分析解剖队列数据的基础，其中特征形状与人口统计学或流行病学数据相关联。由几千个对象组成的 SSM 与统计方法或机器学习技术相结合，提供了识别特征聚类的可能性，从而为高级诊断疾病评分奠定了基础。
