# Residual Aligner Network

arXiv: https://arxiv.org/abs/2203.04290 · HTML: https://mlsanity.com/paper/2203.04290/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [Networks for Joint Affine and Non-parametric Image Registration](https://mlsanity.com/paper/1903.08811/) (2019)
- [Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks](https://mlsanity.com/paper/1907.10931/) (2019)
- [Cross-modal Attention for MRI and Ultrasound Volume Registration](https://mlsanity.com/paper/2107.04548/) (2021)
- [Taking a Deeper Look at the Inverse Compositional Algorithm](https://mlsanity.com/paper/1812.06861/) (2018)
- [Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond](https://mlsanity.com/paper/2004.14557/) (2020)
- [A Deep Discontinuity-Preserving Image Registration Network](https://mlsanity.com/paper/2107.04440/) (2021)
- [DeepReg: a deep learning toolkit for medical image registration](https://mlsanity.com/paper/2011.02580/) (2020)

## Built on by (most cited first, 10 of 10)

- [VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module](https://mlsanity.com/paper/2404.05105/) (2024)
- [ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration](https://mlsanity.com/paper/2403.16526/) (2024)
- [UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2410.20348/) (2024)
- [Unsupervised learning of spatially varying regularization for diffeomorphic image registration](https://mlsanity.com/paper/2412.17982/) (2024)
- [Pyramid Attention Network for Medical Image Registration](https://mlsanity.com/paper/2402.09016/) (2024)
- [Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss](https://mlsanity.com/paper/2406.15172/) (2024)
- [Deformable Image Registration with Multi-scale Feature Fusion from Shared Encoder, Auxiliary and Pyramid Decoders](https://mlsanity.com/paper/2408.05717/) (2024)
- [NCF: Neural Correspondence Field for Medical Image Registration](https://mlsanity.com/paper/2503.00760/) (2025)
- [Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network](https://mlsanity.com/paper/2506.19222/) (2025)
- [Data-driven registration and modeling of brain deformation for image-guided neurosurgery](https://mlsanity.com/paper/2602.10155/) (2026)

## Abstract (arXiv)

Image registration is important for medical imaging, the estimation of the spatial transformation between different images. Many previous studies have used learning-based methods for coarse-to-fine registration to efficiently perform 3D image registration. The coarse-to-fine approach, however, is limited when dealing with the different motions of nearby objects. Here we propose a novel Motion-Aware (MA) structure that captures the different motions in a region. The MA structure incorporates a novel Residual Aligner (RA) module which predicts the multi-head displacement field used to disentangle the different motions of multiple neighbouring objects. Compared with other deep learning methods, the network based on the MA structure and RA module achieve one of the most accurate unsupervised inter-subject registration on the 9 organs of assorted sizes in abdominal CT scans, with the highest-ranked registration of the veins (Dice Similarity Coefficient / Average surface distance: 62%/4.9mm for the vena cava and 34%/7.9mm for the portal and splenic vein), with a half-sized structure and more efficient computation. Applied to the segmentation of lungs in chest CT scans, the new network achieves results which were indistinguishable from the best-ranked networks (94%/3.0mm). Additionally, the theorem on predicted motion pattern and the design of MA structure are validated by further analysis.