An Unsupervised Registration Method for Brain Images Based on Contour Guidance

For medical image registration with large deformation, the huge differences in shape, position and texture make this task challenging. At present, unsupervised registration methods usually use registration software or traditional registration methods for preprocessing, thus only focusing on nonlinear transformation. This paper designs an unsupervised affine registration network to achieve end-to-end registration. The algorithm consists of two parts: (1) Use the designed affine network to predict affine parameters. (2) Use the affine transformer to perform affine transformation on the image. To make the network converge faster, we design a parameter regularization loss for affine parameter constraint. We tested our method on two datasets, including a private dataset and a public dataset, BraTS2018. We use normalized cross-correlation (NCC), dice similarity coefficient (DSC) and mean square error (MSE) as evaluation metrics. Compared with some commonly used unsupervised methods, our method achieves the best results in these three evaluation metrics. Through qualitative analysis, our method can register large deformation image well.