Deep Learning based deformable registration techniques such as Voxelmorph, ICNet, FIRE, do not explicitly encode global dependencies and track large deformations. This research attempts to encode semantics, i.e. structure and overall view of the anatomy in the supplied image, by incorporating self-constructing graph network in the latent space of a UNet model. It also attempts to track larger deformations through multiscale architecture and maintains consistent deformations through cycle consistency. The proposed method was compared against Voxelmorph and ANTs for T1 intramodal and T1-T2 Intermodal registration on IXI Dataset. The experiments show that the proposed model outperforms the baselines.
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