Kim Minjeong, Wu Guorong, Rekik Isrem, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Mach Learn Med Imaging. 2016 Oct;10019:69-76. doi: 10.1007/978-3-319-47157-0_9. Epub 2016 Oct 1.
The growing collection of longitudinal images for brain disease diagnosis necessitates the development of advanced longitudinal registration and anatomical labeling methods that can respect temporal consistency between images. However, the characteristics of such longitudinal images and how they lodge into the image manifold are often neglected in existing labeling methods. Indeed, most of them independently align atlases to each target time-point image for propagating the pre-defined atlas labels to the subject domain. In this paper, we present a - to consistently label anatomical regions of interest in brain images across different time-points using a multi-atlases-based labeling framework. Our framework can best enhance the labeling of longitudinal images through: using the group mean of the longitudinal images of each subject (i.e., subject-mean) as a bridge between atlases and the longitudinal subject scans to align atlases to all time-point images jointly; and using inter-atlas relationship in their nesting manifold to better register each atlas image to the subject-mean. These steps yield to a more consistent (from the joint alignment of atlases with all time-point images) and more accurate (from the manifold-guided registration between each atlases and the subject-mean image) registration, thereby eventually improving the consistency and accuracy for the subsequent labeling step. We have tested our dual-layer groupwise registration method to label two challenging longitudinal brain datasets (i.e., healthy infants and Alzheimer's disease subjects). Our experimental results have showed that our method achieves higher labeling accuracy while keeping the labeling consistency over time, when compared to the traditional registration scheme (without our proposed contributions). Moreover, the proposed framework can flexibly integrate with the existing label fusion methods, such as sparse-patch based methods, to improve the labeling accuracy of longitudinal datasets.
用于脑部疾病诊断的纵向图像集不断增加，这就需要开发先进的纵向配准和解剖标记方法，这些方法要能尊重图像之间的时间一致性。然而，现有的标记方法往往忽略了此类纵向图像的特征以及它们如何嵌入图像流形。实际上，大多数方法都是将图谱独立地与每个目标时间点图像对齐，以便将预定义的图谱标签传播到受试者领域。在本文中，我们提出了一种基于多图谱的标记框架，以在不同时间点一致地标记脑部图像中的感兴趣解剖区域。我们的框架可以通过以下方式最佳地增强纵向图像的标记：将每个受试者纵向图像的组均值（即受试者均值）用作图谱与纵向受试者扫描之间的桥梁，以将图谱联合对齐到所有时间点图像；以及利用图谱在其嵌套流形中的相互关系，将每个图谱图像更好地配准到受试者均值。这些步骤产生了更一致的（来自图谱与所有时间点图像的联合对齐）和更准确的（来自每个图谱与受试者均值图像之间的流形引导配准）配准，从而最终提高了后续标记步骤的一致性和准确性。我们已经测试了我们的双层组内配准方法，以标记两个具有挑战性的纵向脑数据集（即健康婴儿和阿尔茨海默病受试者）。我们的实验结果表明，与传统配准方案（没有我们提出的贡献）相比，我们的方法在保持随时间的标记一致性的同时，实现了更高的标记准确性。此外，所提出的框架可以灵活地与现有的标签融合方法（如基于稀疏补丁的方法）集成，以提高纵向数据集的标记准确性。
