ASymReg: Robust symmetric image registration using anti-symmetric formulation and deformation inversion layers
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Deep learning based deformable medical image registration methods have emerged as a strong alternative for classical iterative registration methods. However, the currently published deep learning methods do not fulfill as strict symmetry properties with respect to the inputs as some classical registration methods, for which the registration outcome is the same regardless of the order of the inputs. While some deep learning methods label themselves as symmetric, they are either symmetric only a priori, which does not guarantee symmetry for any given input pair, or they do not generate accurate explicit inverses. In this work, we propose a novel registration architecture which by construction makes the registration network anti-symmetric with respect to its inputs. We demonstrate on two datasets that the proposed method achieves state-of-the-art results in terms of registration accuracy and that the generated deformations have accurate explicit inverses.
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