  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.
