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Deformable templates are essential to large-scale medical image registration,segmentation, and population analysis. Current conventional and deepnetwork-based methods for template construction use only regularizedregistration objectives and often yield templates with blurry and/oranatomically implausible appearance, confounding downstream biomedicalinterpretation. We reformulate deformable registration and conditional templateestimation as an adversarial game wherein we encourage realism in the movedtemplates with a generative adversarial registration framework conditioned onflexible image covariates. The resulting templates exhibit significant gain inspecificity to attributes such as age and disease, better fit underlyinggroup-wise spatiotemporal trends, and achieve improved sharpness andcentrality. These improvements enable more accurate population modeling withdiverse covariates for standardized downstream analyses and easier anatomicaldelineation for structures of interest.

Generative Adversarial Registration for Improved Conditional Deformable Templates