  Accurate registration of medical images is vital for doctor's diagnosis and
quantitative analysis. In this paper, we propose a new deformable medical image
registration method based on average geometric transformations and VoxelMorph
CNN architecture. We compute the differential geometric information including
Jacobian determinant(JD) and the curl vector(CV) of diffeomorphic registration
field and use them as multi-channel of VoxelMorph CNN for second train. In
addition, we use the average transformation to construct a standard brain MRI
atlas which can be used as fixed image. We verify our method on two datasets
including ADNI dataset and MRBrainS18 Challenge dataset, and obtain excellent
improvement on MR image registration with average Dice scores and non-negative
Jacobian locations compared with MIT's original method. The experimental
results show the method can achieve better performance in brain MRI diagnosis.
