# Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

arXiv: https://arxiv.org/abs/2011.06216 · HTML: https://mlsanity.com/paper/2011.06216/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2106.14591/) (2021)
- [Multi-modal unsupervised brain image registration using edge maps](https://mlsanity.com/paper/2202.04647/) (2022)
- [Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification](https://mlsanity.com/paper/2207.07303/) (2022)

## Abstract (arXiv)

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.