Combining morphological information in a manifold learning framework: application to neonatal MRI.
Combining morphological information in a manifold learning framework: application to neonatal MRI.
复制标题
在流形学习框架中结合形态信息：在新生儿 MRI 中的应用。
DOI：
10.1007/978-3-642-15711-0_1
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Aljabar P
中科院分区：
文献类型：
--
作者：
Aljabar P
MR图像数据可以提供许多特征或测量，尽管任何单个测量不太可能全面表征潜在形态。我们提出了一个框架，在该框架中，在多个学习步骤中使用多个措施来生成坐标嵌入，然后将其结合起来，以给出改进的单一表示的人口。新生儿脑MRI数据的应用表明，使用的形状和外观的措施，特别是导致生物合理的和一致的表示以及相关的临床数据。相关性之间的相似性表明嵌入分量与相对独立的形态特征有关。在新生儿大脑发育过程中大脑形状和MR图像外观发生的快速变化证明了形状测量（从变形度量获得）和外观测量（从图像相似性获得）的使用是合理的。通过改善与临床数据的相关性证明了结合单独嵌入的好处，并且我们说明了所提出的框架在表征大脑发育轨迹方面的潜力。
MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.