  In recent years, learning-based image registration methods have gradually
moved away from direct supervision with target warps to instead use
self-supervision, with excellent results in several registration benchmarks.
These approaches utilize a loss function that penalizes the intensity
differences between the fixed and moving images, along with a suitable
regularizer on the deformation. In this paper, we argue that the relative
failure of supervised registration approaches can in part be blamed on the use
of regular U-Nets, which are jointly tasked with feature extraction, feature
matching, and estimation of deformation. We introduce one simple but crucial
modification to the U-Net that disentangles feature extraction and matching
from deformation prediction, allowing the U-Net to warp the features, across
levels, as the deformation field is evolved. With this modification, direct
supervision using target warps begins to outperform self-supervision approaches
that require segmentations, presenting new directions for registration when
images do not have segmentations. We hope that our findings in this preliminary
workshop paper will re-ignite research interest in supervised image
registration techniques. Our code is publicly available from
https://github.com/balbasty/superwarp.
