Rueckert Daniel, Frangi Alejandro F, Schnabel Julia A
Department of Computing, Imperial College, London, UK.
IEEE Trans Med Imaging. 2003 Aug;22(8):1014-25. doi: 10.1109/TMI.2003.815865.
In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to statistical shape models (SSMs) which capture statistical information about shapes across a population, but offers several advantages over SSMs. First, SDMs can be constructed directly from images such as three-dimensional (3-D) magnetic resonance (MR) or computer tomography volumes without the need for segmentation which is usually a prerequisite for the construction of SSMs. Instead, a nonrigid registration algorithm based on free-form deformations and normalized mutual information is used to compute the deformations required to establish dense correspondences between the reference subject and the subjects in the population class under investigation. Second, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3-D nature of the underlying anatomy into account by analysing dense 3-D deformation fields rather than only information about the surface shape of anatomical structures. We show results for the construction of anatomical models of the brain from the MR images of 25 different subjects. The correspondences obtained by the nonrigid registration are evaluated using anatomical landmark locations and show an average error of 1.40 mm at these anatomical landmark positions. We also demonstrate that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.
在本文中，我们展示了统计变形模型（SDMs）的概念如何用于构建解剖结构的平均模型及其变异性。通过对将一个受试者的解剖特征映射到另一个受试者的相应特征所需的变形进行统计分析来构建SDMs。SDMs的概念类似于统计形状模型（SSMs），后者捕获了整个人口中形状的统计信息，但比SSMs具有几个优势。首先，SDMs可以直接从诸如三维（3-D）磁共振（MR）或计算机断层扫描容积等图像构建，而无需分割，分割通常是构建SSMs的先决条件。相反，基于自由形式变形和归一化互信息的非刚性配准算法用于计算在参考受试者与所研究的人群类别中的受试者之间建立密集对应所需的变形。其次，SDMs允许构建平均解剖结构的图谱及其在一群受试者中的变异性。最后，SDMs通过分析密集的3-D变形场而不是仅关于解剖结构表面形状的信息来考虑基础解剖结构的3-D性质。我们展示了从25个不同受试者的MR图像构建大脑解剖模型的结果。使用解剖标志位置评估通过非刚性配准获得的对应关系，在这些解剖标志位置显示平均误差为1.40毫米。我们还证明可以构建SDMs以最小化对所选参考受试者的偏差。
