# GAN Based Medical Image Registration

arXiv: https://arxiv.org/abs/1805.02369 · HTML: https://mlsanity.com/paper/1805.02369/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 9 of 9)

- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation](https://mlsanity.com/paper/2110.00404/) (2021)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning](https://mlsanity.com/paper/2111.07646/) (2021)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)
- [Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis](https://mlsanity.com/paper/2211.08424/) (2022)

## Abstract (arXiv)

Conventional approaches to image registration consist of time consuming iterative methods. Most current deep learning (DL) based registration methods extract deep features to use in an iterative setting. We propose an end-to-end DL method for registering multimodal images. Our approach uses generative adversarial networks (GANs) that eliminates the need for time consuming iterative methods, and directly generates the registered image with the deformation field. Appropriate constraints in the GAN cost function produce accurately registered images in less than a second. Experiments demonstrate their accuracy for multimodal retinal and cardiac MR image registration.