# A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond

arXiv: https://arxiv.org/abs/2307.15615 · HTML: https://mlsanity.com/paper/2307.15615/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 190 of its 495 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 30 of 190)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)

## Built on by (most cited first, 30 of 55)

- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [multiGradICON: A Foundation Model for Multimodal Medical Image Registration](https://mlsanity.com/paper/2408.00221/) (2024)
- [Beyond the LUMIR challenge: The pathway to foundational registration models](https://mlsanity.com/paper/2505.24160/) (2025)
- [Vector Field Attention for Deformable Image Registration](https://mlsanity.com/paper/2407.10209/) (2024)
- [DINOv3 with Test-Time Training for Medical Image Registration](https://mlsanity.com/paper/2508.14809/) (2025)
- [IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration](https://mlsanity.com/paper/2503.24121/) (2025)
- [Conformal Lesion Segmentation for 3D Medical Images](https://mlsanity.com/paper/2510.17897/) (2025)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [From Registration Uncertainty to Segmentation Uncertainty](https://mlsanity.com/paper/2403.05111/) (2024)
- [Pretraining Deformable Image Registration Networks with Random Images](https://mlsanity.com/paper/2505.24167/) (2025)
- [Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis](https://mlsanity.com/paper/2601.02409/) (2026)
- [Learning Domain-Invariant Representations for Cross-Domain Image Registration via Scene-Appearance Disentanglement](https://mlsanity.com/paper/2601.08875/) (2026)
- [CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image Registration](https://mlsanity.com/paper/2604.05689/) (2026)
- [MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction](https://mlsanity.com/paper/2308.02949/) (2023)
- [Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?](https://mlsanity.com/paper/2401.17571/) (2024)
- [CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration](https://mlsanity.com/paper/2410.02316/) (2024)
- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)
- [Unsupervised learning of spatially varying regularization for diffeomorphic image registration](https://mlsanity.com/paper/2412.17982/) (2024)
- [Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging](https://mlsanity.com/paper/2503.16543/) (2025)
- [Learning to Select Like Humans: Explainable Active Learning for Medical Imaging](https://mlsanity.com/paper/2602.13308/) (2026)
- [Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention](https://mlsanity.com/paper/2412.18545/) (2024)
- [Spatial regularisation for improved accuracy and interpretability in keypoint-based registration](https://mlsanity.com/paper/2503.04499/) (2025)
- [Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration](https://mlsanity.com/paper/2506.15596/) (2025)
- [Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion](https://mlsanity.com/paper/2507.23508/) (2025)
- [The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment](https://mlsanity.com/paper/2511.08328/) (2025)
- [Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)](https://mlsanity.com/paper/2604.06482/) (2026)
- [VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence](https://mlsanity.com/paper/2605.13798/) (2026)
- [Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification](https://mlsanity.com/paper/2607.02585/) (2026)
- [H-SGANet: Hybrid Sparse Graph Attention Network for Deformable Medical Image Registration](https://mlsanity.com/paper/2408.16719/) (2024)

## Abstract (arXiv)

Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, network architectures, and uncertainty estimation. These advancements have not only enriched the field of image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration.