# Fast Predictive Image Registration

arXiv: https://arxiv.org/abs/1607.02504 · HTML: https://mlsanity.com/paper/1607.02504/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2016.

## Built on by (most cited first, 15 of 15)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [ICON: Learning Regular Maps Through Inverse Consistency](https://mlsanity.com/paper/2105.04459/) (2021)
- [Anatomy-aware and acquisition-agnostic joint registration with SynthMorph](https://mlsanity.com/paper/2301.11329/) (2023)
- [Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling](https://mlsanity.com/paper/2109.13630/) (2021)
- [Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration](https://mlsanity.com/paper/2112.07415/) (2021)
- [JOSA: Joint surface-based registration and atlas construction of brain geometry and function](https://mlsanity.com/paper/2311.08544/) (2023)
- [EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration](https://mlsanity.com/paper/2403.11026/) (2024)
- [Joint cortical registration of geometry and function using semi-supervised learning](https://mlsanity.com/paper/2303.01592/) (2023)
- [Recurrent Inference Machine for Medical Image Registration](https://mlsanity.com/paper/2406.13413/) (2024)
- [CoRLD: Contrastive Representation Learning Of Deformable Shapes In Images](https://mlsanity.com/paper/2503.17162/) (2025)
- [Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography](https://mlsanity.com/paper/2309.00831/) (2023)
- [Application-driven Validation of Posteriors in Inverse Problems](https://mlsanity.com/paper/2309.09764/) (2023)
- [RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13672/) (2023)
- [MorphSeek: Fine-grained Latent Representation-Level Policy Optimization for Deformable Image Registration](https://mlsanity.com/paper/2511.17392/) (2025)
- [LDRNet: Large Deformation Registration Model for Chest CT Registration](https://mlsanity.com/paper/2602.01812/) (2026)

## Abstract (arXiv)

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.