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Deep learning based methods provide efficient solutions to medical imageregistration, including the challenging problem of diffeomorphic imageregistration. However, most methods register normal image pairs, facingdifficulty handling those with missing correspondences, e.g., in the presenceof pathology like tumors. We desire an efficient solution to jointly accountfor spatial deformations and appearance changes in the pathological regionswhere the correspondences are missing, i.e., finding a solution to metamorphicimage registration. Some approaches are proposed to tackle this problem, butthey cannot properly handle large pathological regions and deformations aroundpathologies. In this paper, we propose a deep metamorphic image registrationnetwork (MetaRegNet), which adopts time-varying flows to drive spatialdiffeomorphic deformations and generate intensity variations. We evaluateMetaRegNet on two datasets, i.e., BraTS 2021 with brain tumors and 3D-IRCADb-01with liver tumors, showing promising results in registering a healthy and tumorimage pair. The source code is available online.

MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks