# Regularized directional representations for medical image registration

arXiv: https://arxiv.org/abs/2111.15509 · HTML: https://mlsanity.com/paper/2111.15509/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [Image-and-Spatial Transformer Networks for Structure-Guided Image Registration](https://mlsanity.com/paper/1907.09200/) (2019)
- [A Deep Metric for Multimodal Registration](https://mlsanity.com/paper/1609.05396/) (2016)
- [Liver segmentation and metastases detection in MR images using convolutional neural networks](https://mlsanity.com/paper/1910.06635/) (2019)

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

- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [CT respiratory motion synthesis using joint supervised and adversarial learning](https://mlsanity.com/paper/2404.00163/) (2024)
- [CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration](https://mlsanity.com/paper/2603.23694/) (2026)

## Abstract (arXiv)

In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a technique we call textitvector field similarity. Our approach can be combined in a straightforward fashion with any existing registration framework by substituting vector field similarity to intensity-based registration. In our experiments, we show that the proposed approach compares favourably with conventional image alignment on several public image datasets using a diversity of imaging modalities and anatomical locations.