eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration.
eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration.
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DOI：
10.1371/journal.pone.0146870
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Wu G;Peng X;Ying S;Wang Q;Yap PT;Shen D;Shen D
大量脑图像的有效和高效的空间归一化对于许多临床和研究来说是至关重要的，但它在技术上非常具有挑战性。常用的方法是选择某个图像作为模板，然后通过应用成对配准将群体中的所有其他图像与该模板对齐。为了避免由不适当的模板选择引起的潜在偏差，已经提出了分组配准方法以同时将所有图像配准到潜在公共空间。然而，目前的分组配准方法没有充分利用图像分布信息进行更准确的配准。在本文中，我们提出了一种新的groupwise配准方法，利用图像分布信息捕获图像分布流形使用层次图，其节点代表个人的图像。更具体地，低级图描述每个子组中的图像分布，并且高级图对子组的代表性图像之间的关系进行编码。给定图表示，我们可以通过动态收缩图像流形上的图来将所有图像注册到公共空间。整个图像分布的拓扑结构在图形收缩期间始终保持不变。对两个数据集（一个针对80名老年人，一个针对285名婴儿）的评估表明，我们的方法可以产生有希望的结果。
Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results.