Dong Pei, Cao Xiaohuan, Zhang Jun, Kim Minjeong, Wu Guorong, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill.
Mach Learn Med Imaging. 2017;10541:150-158. doi: 10.1007/978-3-319-67389-9_18. Epub 2017 Sep 7.
Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps. First, a set of training brain MR images is registered to their group center with a certain existing groupwise registration method. Then, in order to augment the samples, we perform PCA on the set of obtained deformation fields (to the group center) to parameterize the deformation fields. In doing so, we can generate a large number of deformation fields, as well as their respective simulated samples using different parameters for PCA. In the application stage, when given a new set of testing brain MR images, we can mix them with the augmented training samples. Then, for each testing image, we can find its closest sample in the augmented training dataset for fast estimating its deformation field to the group center of the training set. In this way, a tentative group center of the testing image set can be immediately estimated, and the deformation field of each testing image to this estimated group center can be obtained. With this fast initialization for groupwise registration of testing images, we can finally use an existing groupwise registration method to quickly refine the groupwise registration results. Experimental results on ADNI dataset show the significantly improved computational efficiency and competitive registration accuracy, compared to state-of-the-art groupwise registration methods.
逐组图像配准为一组图像提供了一种无偏的配准解决方案，这有助于后续的群体分析。然而，对大量图像进行逐组配准通常在计算上代价高昂。为了缓解这个问题，我们提出利用一种快速初始化技术来加速逐组配准。我们的主要思想是生成一组到其组中心具有已知变形的模拟脑磁共振成像样本。这可以在训练阶段通过两个步骤实现。首先，使用某种现有的逐组配准方法将一组训练脑磁共振图像配准到它们的组中心。然后，为了扩充样本，我们对获得的（到组中心的）变形场集执行主成分分析（PCA）以对变形场进行参数化。这样做，我们可以生成大量的变形场以及使用不同PCA参数的各自模拟样本。在应用阶段，当给定一组新的测试脑磁共振图像时，我们可以将它们与扩充后的训练样本混合。然后，对于每个测试图像，我们可以在扩充后的训练数据集中找到其最接近的样本，以便快速估计其到训练集组中心的变形场。通过这种方式，可以立即估计测试图像集的暂定组中心，并获得每个测试图像到这个估计组中心的变形场。通过对测试图像的逐组配准进行这种快速初始化，我们最终可以使用现有的逐组配准方法快速优化逐组配准结果。在ADNI数据集上的实验结果表明，与最先进的逐组配准方法相比，计算效率显著提高，配准精度具有竞争力。
