Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation

• URL: http://arxiv.org/abs/2510.22380v1
• Date: Sat, 25 Oct 2025 17:49:29 GMT
• Title: Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation
• Authors: Tianran Li, Marius Staring, Yuchuan Qiao,
• Abstract summary: Convolutional networks aggregate local features but lack direct modeling of voxel correspondences.<n>We propose a Recurrent Correlation-based matching framework, supporting efficient matching toward large deformations.<n>We conduct experiments on brain MRI and abdominal CT datasets under two settings: with and without affine pre-registration.
• Score: 3.0110453390062446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable image registration estimates voxel-wise correspondences between images through spatial transformations, and plays a key role in medical imaging. While deep learning methods have significantly reduced runtime, efficiently handling large deformations remains a challenging task. Convolutional networks aggregate local features but lack direct modeling of voxel correspondences, promoting recent works to explore explicit feature matching. Among them, voxel-to-region matching is more efficient for direct correspondence modeling by computing local correlation features whithin neighbourhoods, while region-to-region matching incurs higher redundancy due to excessive correlation pairs across large regions. However, the inherent locality of voxel-to-region matching hinders the capture of long-range correspondences required for large deformations. To address this, we propose a Recurrent Correlation-based framework that dynamically relocates the matching region toward more promising positions. At each step, local matching is performed with low cost, and the estimated offset guides the next search region, supporting efficient convergence toward large deformations. In addition, we uses a lightweight recurrent update module with memory capacity and decouples motion-related and texture features to suppress semantic redundancy. We conduct extensive experiments on brain MRI and abdominal CT datasets under two settings: with and without affine pre-registration. Results show that our method exibits a strong accuracy-computation trade-off, surpassing or matching the state-of-the-art performance. For example, it achieves comparable performance on the non-affine OASIS dataset, while using only 9.5% of the FLOPs and running 96% faster than RDP, a representative high-performing method.

Related papers

• Event-based Visual Deformation Measurement [76.25283405575108]
  Visual Deformation Measurement aims to recover dense deformation fields by tracking surface motion from camera observations.<n>Traditional image-based methods rely on minimal inter-frame motion to constrain the correspondence search space.<n>We propose an event-frame fusion framework that exploits events for temporally dense motion cues and frames for spatially dense precise estimation.
  arXiv  Detail & Related papers  (2026-02-16T01:04:48Z)
• Graph Laplacian Transformer with Progressive Sampling for Prostate Cancer Grading [2.9485900021889146]
  We propose a Graph Laplacian Attention-Based Transformer (GLAT) integrated with an Iterative Refinement Module (IRM) to enhance both feature learning and spatial consistency.<n>IRM iteratively refines patch selection by leveraging a pretrained ResNet50 for local feature extraction and a foundation model in no-gradient mode for importance scoring.<n>The GLAT models tissue-level connectivity by constructing a graph where patches serve as nodes, ensuring spatial consistency through graph Laplacian constraints.
  arXiv  Detail & Related papers  (2025-12-11T16:55:57Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• EDFFDNet: Towards Accurate and Efficient Unsupervised Multi-Grid Image Registration [17.190325630307097]
  We propose an Exponential-Decay Free-Form Deformation Network (EDFFDNet), which employs free-form deformation with an exponential-decay basis function.<n>By transforming dense interactions into sparse ones, ASMA reduces parameters and improves accuracy.<n>Experiments demonstrate that EDFFDNet reduces parameters, memory, and total runtime by 70.5%, 32.6%, and 33.7%, respectively.<n>EDFFDNet-2 further improves PSNR by 1.06 dB while maintaining lower computational costs.
  arXiv  Detail & Related papers  (2025-09-09T12:30:51Z)
• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement [80.94378602238432]
  We propose an efficient structure named Correspondence Efficient Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing state-of-the-arts.
  arXiv  Detail & Related papers  (2022-09-25T13:05:33Z)
• Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition [54.23513799338309]
  We present an Adaptive Local-Component-aware Graph Convolutional Network for skeleton-based action recognition. Our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
  arXiv  Detail & Related papers  (2022-09-21T02:33:07Z)
• Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation [4.562196564569076]
  generative adversarial network (GAN-IT) is a promising method for precise localization of abnormal regions in chest X-ray images (AL-CXR) We propose an improved two-stage GAN-IT involving registration and data augmentation.
  arXiv  Detail & Related papers  (2022-07-21T06:42:12Z)
• DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference [86.03382625531951]
  DANCE is an automated simultaneous data-network co-optimization for efficient segmentation model training and inference.<n>It integrates automated data slimming which adaptively downsamples/drops input images and controls their corresponding contribution to the training loss guided by the images' spatial complexity.<n>Experiments and ablating studies demonstrate that DANCE can achieve "all-win" towards efficient segmentation.
  arXiv  Detail & Related papers  (2021-07-16T04:58:58Z)
• FasterPose: A Faster Simple Baseline for Human Pose Estimation [65.8413964785972]
  We propose a design paradigm for cost-effective network with LR representation for efficient pose estimation, named FasterPose. We study the training behavior of FasterPose, and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence. Compared with the previously dominant network of pose estimation, our method reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy.
  arXiv  Detail & Related papers  (2021-07-07T13:39:08Z)
• Multiple Object Tracking with Correlation Learning [16.959379957515974]
  We propose to exploit the local correlation module to model the topological relationship between targets and their surrounding environment. Specifically, we establish dense correspondences of each spatial location and its context, and explicitly constrain the correlation volumes through self-supervised learning. Our approach demonstrates the effectiveness of correlation learning with the superior performance and obtains state-of-the-art MOTA of 76.5% and IDF1 of 73.6% on MOT17.
  arXiv  Detail & Related papers  (2021-04-08T06:48:02Z)
• Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence [2.6894568533991543]
  We introduce an auxiliary input to the neural network for the prior information about the prostate location in the MR sequence. With weakly labelled MR-TRUS prostate data, we showed registration quality comparable to the state-of-the-art deep learning-based method.
  arXiv  Detail & Related papers  (2021-02-09T16:48:59Z)
• Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization [30.203072945001136]
  In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMUSeasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision.
  arXiv  Detail & Related papers  (2020-09-16T14:43:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.