# Medical image registration using unsupervised deep neural network: A scoping literature review arXiv: https://arxiv.org/abs/2208.01825 ยท HTML: https://mlsanity.com/paper/2208.01825/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 113 references that are in the map. - First posted on arXiv 3 Aug 2022. ## Builds on (most cited first, 30 of 40) - [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015) - [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017) - [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018) - [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017) - [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019) - [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018) - [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018) - [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019) - [Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration](https://mlsanity.com/paper/1805.04605/) (2018) - [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019) - [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017) - [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020) - [Learning a Probabilistic Model for Diffeomorphic Registration](https://mlsanity.com/paper/1812.07460/) (2018) - [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019) - [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019) - [A Gentle Introduction to Deep Learning in Medical Image Processing](https://mlsanity.com/paper/1810.05401/) (2018) - [Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration](https://mlsanity.com/paper/1809.03443/) (2018) - [mlVIRNET: Multilevel Variational Image Registration Network](https://mlsanity.com/paper/1909.10084/) (2019) - [Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration](https://mlsanity.com/paper/1804.07172/) (2018) - [Unsupervised Deformable Image Registration Using Cycle-Consistent CNN](https://mlsanity.com/paper/1907.01319/) (2019) - [Medical Image Registration Using Deep Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2002.03401/) (2020) - [Learning Deformable Registration of Medical Images with Anatomical Constraints](https://mlsanity.com/paper/2001.07183/) (2020) - [Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration](https://mlsanity.com/paper/1907.01922/) (2019) - [Non-rigid image registration using fully convolutional networks with deep self-supervision](https://mlsanity.com/paper/1709.00799/) (2017) - [Cortical surface registration using unsupervised learning](https://mlsanity.com/paper/2004.04617/) (2020) - [GAN Based Medical Image Registration](https://mlsanity.com/paper/1805.02369/) (2018) - [Linear and Deformable Image Registration with 3D Convolutional Neural Networks](https://mlsanity.com/paper/1809.06226/) (2018) - [Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors](https://mlsanity.com/paper/1907.13020/) (2019) - [A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration](https://mlsanity.com/paper/1908.05825/) (2019) - [Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy](https://mlsanity.com/paper/1906.12223/) (2019) ## Built on by (most cited first, 5 of 5) - [DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration](https://mlsanity.com/paper/2410.05234/) (2024) - [Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients](https://mlsanity.com/paper/2211.11025/) (2022) - [HNAS-reg: hierarchical neural architecture search for deformable medical image registration](https://mlsanity.com/paper/2308.12440/) (2023) - [Deep learning in medical image registration: introduction and survey](https://mlsanity.com/paper/2309.00727/) (2023) - [QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration](https://mlsanity.com/paper/2310.00296/) (2023) ## Abstract (arXiv) In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field.