  Deformable templates are essential to large-scale medical image registration,
segmentation, and population analysis. Current conventional and deep
network-based methods for template construction use only regularized
registration objectives and often yield templates with blurry and/or
anatomically implausible appearance, confounding downstream biomedical
interpretation. We reformulate deformable registration and conditional template
estimation as an adversarial game wherein we encourage realism in the moved
templates with a generative adversarial registration framework conditioned on
flexible image covariates. The resulting templates exhibit significant gain in
specificity to attributes such as age and disease, better fit underlying
group-wise spatiotemporal trends, and achieve improved sharpness and
centrality. These improvements enable more accurate population modeling with
diverse covariates for standardized downstream analyses and easier anatomical
delineation for structures of interest.
