Objective: Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it hard to accurately align corresponding cortical structures of individual images. In this paper, we propose a novel deep learning way to simplify the difficult registration problem of brain MR images. Methods: We train a morphological simplification network (MS-Net), which can generate a "simple" image with less anatomical details based on the "complex" input. With MS-Net, the complexity of the fixed image or the moving image under registration can be reduced gradually, thus building an individual (simplification) trajectory represented by MS-Net outputs. Since the generated images at the ends of the two trajectories (of the fixed and moving images) are so simple and very similar in appearance, they are easy to register. Thus, the two trajectories can act as a bridge to link the fixed and the moving images, and guide their registration. Results: Our experiments show that the proposed method can achieve highly accurate registration performance on different datasets (i.e., NIREP, LPBA, IBSR, CUMC, and MGH). Moreover, the method can be also easily transferred across diverse image datasets and obtain superior accuracy on surface alignment. Conclusion and Significance: We propose MS-Net as a powerful and flexible tool to simplify brain MR images and their registration. To our knowledge, this is the first work to simplify brain MR image registration by deep learning, instead of estimating deformation field directly.
翻译:目标: 变形的大脑 MR 图像注册具有挑战性, 原因是在“ 复合” 输入的基础上产生“ 简单” 图像, 且不那么解剖细节。 通过 MS- Net, 固定图像或正在注册的移动图像的复杂性可以逐渐降低, 从而建立MS- Net 输出所代表的个人( 简化) 轨迹( 简化) 。 在本文中, 我们提出一个新的深层次的学习方法, 以简化大脑 MR 图像的注册问题。 方法: 我们训练一个形态简化网络( MS- Net), 可以生成一个基于“ 复合” 输入的“ 简单” 图像, 产生一个“ 简单” 图像, 且不那么解解解解解解解解解的图像。 通过 MS- Net, 我们的实验显示, 固定图像或正在注册中移动的图像, 能够在不同的数据设置上实现高度准确的注册( IM-) (i. IMA, 直接 和 IMLPL IM IM IM 的注册,, 和 IML IM IM 等, 系统, 系统 系统, 可以 快速, 和 C, 的注册, 和 系统, 和 系统 系统 进行,, 系统 系统 和 和 系统 系统 进行 进行 。