# A Deep Discontinuity-Preserving Image Registration Network

arXiv: https://arxiv.org/abs/2107.04440 · HTML: https://mlsanity.com/paper/2107.04440/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [Learning a Probabilistic Model for Diffeomorphic Registration](https://mlsanity.com/paper/1812.07460/) (2018)

## Built on by (most cited first, 17 of 17)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023)
- [MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters](https://mlsanity.com/paper/2407.08093/) (2024)
- [Residual Aligner Network](https://mlsanity.com/paper/2203.04290/) (2022)
- [Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2207.03180/) (2022)
- [Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images](https://mlsanity.com/paper/2211.13828/) (2022)
- [GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration](https://mlsanity.com/paper/2306.14687/) (2023)
- [Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization](https://mlsanity.com/paper/2409.05040/) (2024)
- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization](https://mlsanity.com/paper/2602.06288/) (2026)
- [Unsupervised learning of spatially varying regularization for diffeomorphic image registration](https://mlsanity.com/paper/2412.17982/) (2024)
- [SAMIR, an efficient registration framework via robust feature learning from SAM](https://mlsanity.com/paper/2509.13629/) (2025)
- [Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024](https://mlsanity.com/paper/2409.00917/) (2024)
- [DAGrid: Directed Accumulator Grid](https://mlsanity.com/paper/2306.02589/) (2023)
- [Ideal Registration? Segmentation is All You Need](https://mlsanity.com/paper/2509.15784/) (2025)
- [FMIR, a foundation model-based Image Registration Framework for Robust Image Registration](https://mlsanity.com/paper/2601.17529/) (2026)
- [Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration](https://mlsanity.com/paper/2601.19114/) (2026)

## Abstract (arXiv)

Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions. Currently, most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous, which is not always valid for real-world scenarios, especially in medical image registration (e.g. cardiac imaging and abdominal imaging). Such a global constraint can lead to artefacts and increased errors at discontinuous tissue interfaces. To tackle this issue, we propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR), to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging Study (UKBB), than state-of-the-art approaches.