SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration

by   Sean I. Young, et al.

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


Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation

Deformation field estimation is an important and challenging issue in ma...

Unsupervised 3D registration through optimization-guided cyclical self-training

State-of-the-art deep learning-based registration methods employ three d...

Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration

Traditional deformable registration techniques achieve impressive result...

WarpPINN: Cine-MR image registration with physics-informed neural networks

Heart failure is typically diagnosed with a global function assessment, ...

DrapeNet: Generating Garments and Draping them with Self-Supervision

Recent approaches to drape garments quickly over arbitrary human bodies ...

Please sign up or login with your details

Forgot password? Click here to reset