Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images (MRI), some deep learning registration methods use segmentation images for training model. However, the segmentation values are constant for each label, which leads to the gradient variation concentrating on the boundary. Thus, the dense deformation field (DDF) is gathered on the boundary and there even appears folding phenomenon. In order to fully leverage the label information, the morphological opening and closing information maps are introduced to enlarge the non-zero gradient regions and improve the accuracy of DDF estimation. The opening information maps supervise the registration model to focus on smaller, narrow brain regions. The closing information maps supervise the registration model to pay more attention to the complex boundary region. Then, opening and closing morphology networks (OC_Net) are designed to automatically generate opening and closing information maps to realize the end-to-end training process. Finally, a new registration architecture, VM_seg+oc, is proposed by combining OC_Net and VoxelMorph. Experimental results show that the registration accuracy of VM_seg+oc is significantly improved on LPBA40 and OASIS1 datasets. Especially, VM_seg+oc can well improve registration accuracy in smaller brain regions and narrow regions.