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This paper presents a novel predictive model, MetaMorph, for metamorphicregistration of images with appearance changes (i.e., caused by brain tumors).In contrast to previous learning-based registration methods that have little orno control over appearance-changes, our model introduces a new regularizationthat can effectively suppress the negative effects of appearance changingareas. In particular, we develop a piecewise regularization on the tangentspace of diffeomorphic transformations (also known as initial velocity fields)via learned segmentation maps of abnormal regions. The geometric transformationand appearance changes are treated as joint tasks that are mutually beneficial.Our model MetaMorph is more robust and accurate when searching for an optimalregistration solution under the guidance of segmentation, which in turnimproves the segmentation performance by providing appropriately augmentedtraining labels. We validate MetaMorph on real 3D human brain tumor magneticresonance imaging (MRI) scans. Experimental results show that our modeloutperforms the state-of-the-art learning-based registration models. Theproposed MetaMorph has great potential in various image-guided clinicalinterventions, e.g., real-time image-guided navigation systems for tumorremoval surgery.

MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes