Multi-scale UNet with Self-Constructing Graph Latent for Deformable Image Registration
Soumick Chatterjee1,2,3, Himanshi Bajaj3, Mohammad Istiyak Hossain Siddiquee3, Nandish Bandi Subbarayappa3, Steve Simon3, Suraj Bangalore Shashidhar3, Oliver Speck1,4,5,6, and Andreas Nürnberger2,3,5
1Department of Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany, 4German Centre for NeurodegenerativeDiseases, Magdeburg, Germany, 5Center for Behavioral Brain Sciences, Magdeburg, Germany, 6Leibniz Institute for Neurobiology, Magdeburg, Germany
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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