# Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information

arXiv: https://arxiv.org/abs/2105.07392 · HTML: https://mlsanity.com/paper/2105.07392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 11 of 11)

- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations](https://mlsanity.com/paper/1903.09331/) (2019)
- [MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation](https://mlsanity.com/paper/2006.15573/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)

## Abstract (arXiv)

Multi-modality medical images can provide relevant or complementary information for a target (organ, tumor or tissue). Registering multi-modality images to a common space can fuse these comprehensive information, and bring convenience for clinical application. Recently, neural networks have been widely investigated to boost registration methods. However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training. In this work, we propose a multi-modality registration network (MMRegNet), which can perform registration between multi-modality images. Meanwhile, we present spatially encoded gradient information to train MMRegNet in an unsupervised manner. The proposed network was evaluated on MM-WHS 2017. Results show that MMRegNet can achieve promising performance for left ventricle cardiac registration tasks. Meanwhile, to demonstrate the versatility of MMRegNet, we further evaluate the method with a liver dataset from CHAOS 2019. Source code will be released publiclyfootnotehttps://github.com/NanYoMy/mmregnet once the manuscript is accepted.