Multi-channel Image Registration of Cardiac MR Using Supervised Feature Learning with Convolutional Encoder-Decoder Network

It is difficult to register the images involving large deformation and intensity inhomogeneity. In this paper, a new multi-channel registration algorithm using modified multi-feature mutual information (α-MI) based on minimal spanning tree (MST) is presented. First, instead of relying on handcrafted features, a convolutional encoder-decoder network is employed to learn the latent feature representation from cardiac MR images. Second, forward computation and backward propagation are performed in a supervised fashion to make the learned features more discriminative. Finally, local features containing appearance information is extracted and integrated into α-MI for achieving multi-channel registration. The proposed method has been evaluated on cardiac cine-MRI data from 100 patients. The experimental results show that features learned from deep network are more effective than handcrafted features in guiding intra-subject registration of cardiac MR images.