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.
CITATION STYLE
Lu, X., & Qiao, Y. (2020). Multi-channel image registration of cardiac mr using supervised feature learning with convolutional encoder-decoder network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12120 LNCS, pp. 103–110). Springer. https://doi.org/10.1007/978-3-030-50120-4_10
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