School of Life Science, Beijing Institute of Technology, Beijing 100081, China; Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Med Image Anal. 2020 Aug;64:101711. doi: 10.1016/j.media.2020.101711. Epub 2020 Jun 10.
Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph. The final atlas is a mean image at the population center of the distribution on the manifold. The procedure of warping all images to the mean image turns to dynamic graph shrinkage in which nodes become closer to each other. Most conventional groupwise registration frameworks construct and shrink a graph without considering the local distribution of images on the dataset manifold and the local structure variations between image pairs. Neglecting the local information fundamentally decrease the accuracy and efficiency when population atlases are built for organs with large inter-subject anatomical variabilities. To overcome the problem, this paper proposes a global-local graph shrinkage approach that can generate accurate atlas. A connected graph is constructed automatically based on global similarities across the images to explore the global distribution. A local image distribution obtained by image clustering is used to simplify the edges of the constructed graph. Subsequently, local image similarities refine the deformation estimated through global image similarity for each image warping along the graph edges. Through the image warping, the overall simplified graph shrinks gradually to yield the atlas with respecting both global and local features. The proposed method is evaluated on 61 synthetic and 20 clinical liver datasets, and the results are compared with those of six state-of-the-art groupwise registration methods. The experimental results show that the proposed method outperforms non-global-local method approaches in terms of accuracy.
基于图的组间配准方法广泛应用于图谱构建中。对于一组图像，可以构建一个图，其中节点表示图像，边表示两个节点之间的测地线路径。通过在图中遍历边，可以探索图像在图像流形上的分布。最终的图谱是分布在流形上的种群中心的平均图像。将所有图像变形到平均图像的过程转化为动态图收缩，其中节点彼此之间变得更加接近。大多数传统的组间配准框架在构建和收缩图时不考虑数据集流形上图像的局部分布和图像对之间的局部结构变化。忽略局部信息会极大地降低构建具有较大个体间解剖变异性的器官群体图谱的准确性和效率。为了解决这个问题，本文提出了一种全局-局部图收缩方法，可以生成准确的图谱。根据图像之间的全局相似性自动构建一个连通图，以探索全局分布。通过图像聚类获得的局部图像分布用于简化构建的图的边。随后，局部图像相似性通过全局图像相似性为每个图像变形进行细化，沿着图的边缘进行变形。通过图像变形，整体简化的图逐渐收缩，生成既尊重全局又尊重局部特征的图谱。在 61 个合成和 20 个临床肝脏数据集上进行了评估，并将结果与六种最先进的组间配准方法进行了比较。实验结果表明，该方法在准确性方面优于非全局-局部方法。
