# Deep Learning in Medical Image Registration: A Review

arXiv: https://arxiv.org/abs/1912.12318 · HTML: https://mlsanity.com/paper/1912.12318/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 30 of 98)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer](https://mlsanity.com/paper/2306.05688/) (2023)
- [The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective](https://mlsanity.com/paper/2108.02238/) (2021)
- [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024)
- [Anatomy-aware and acquisition-agnostic joint registration with SynthMorph](https://mlsanity.com/paper/2301.11329/) (2023)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies](https://mlsanity.com/paper/2412.06784/) (2024)
- [LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging](https://mlsanity.com/paper/2107.09060/) (2021)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer](https://mlsanity.com/paper/2105.01844/) (2021)
- [Deep Learning for Medical Image Registration: A Comprehensive Review](https://mlsanity.com/paper/2204.11341/) (2022)
- [Synthetic CT image generation from CBCT: A Systematic Review](https://mlsanity.com/paper/2501.13972/) (2025)
- [Closed-Form Diffeomorphic Transformations for Time Series Alignment](https://mlsanity.com/paper/2206.08107/) (2022)
- [Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans](https://mlsanity.com/paper/2106.07608/) (2021)
- [Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment](https://mlsanity.com/paper/2106.14699/) (2021)
- [Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models](https://mlsanity.com/paper/2506.02419/) (2025)
- [AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI](https://mlsanity.com/paper/2205.05851/) (2022)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Deformation equivariant cross-modality image synthesis with paired non-aligned training data](https://mlsanity.com/paper/2208.12491/) (2022)
- [CartiMorph: a framework for automated knee articular cartilage morphometrics](https://mlsanity.com/paper/2308.01981/) (2023)
- [Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data](https://mlsanity.com/paper/2308.14925/) (2023)
- [Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration](https://mlsanity.com/paper/2406.07361/) (2024)
- [MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network](https://mlsanity.com/paper/2111.12123/) (2021)
- [Meta-Learning Initializations for Interactive Medical Image Registration](https://mlsanity.com/paper/2210.15371/) (2022)
- [Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"](https://mlsanity.com/paper/2307.06322/) (2023)
- [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://mlsanity.com/paper/2309.17192/) (2023)
- [An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES](https://mlsanity.com/paper/2402.05642/) (2024)

## Abstract (arXiv)

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.