Multimodal MR image registration using weakly supervised constrained affine network
Xiaoyan Wang, Lizhao Mao, Xiaojie Huang, Ming Xia, Zheng Gu · Journal of Modern Optics · 2021
Multimodal image registration is an important technique for many clinical applications. However, it is particularly challenging to obtain good spatial alignment. This paper introduces a novel architecture named the constrained affine network, which combines deformable image registration with affine transformation for multimodal MR image registration. A weakly supervised manner is adapted to train the network and anatomical labels are used in training. The network directly learns to predict a displacement vector field (DVF) between pairs of input images. Different from the existing deformable image registration methods based on the convolutional neural network (CNN), the method proposes a global constrained affine module, which can predict an affine transformation by pre-computing the range of affine parameters, and the model can be combined with a deformable registration network. We evaluated the proposed method on 3D multimodal medical images. Experimental results indicate that the proposed method has better performance.