Deformable image registration (DIR) is a core problem in medical image analysis; but, unlike labeling decision problems such as classification and segmentation, registration is a problem class that involves stringent physical constraints. Although deep learning methods have made faster registration possible, the resulting models are often difficult to interpret compared to hand-crafted methods with explicit objectives and interpretable physical meaning.
arXiv:2603. 17415v2 Announce Type: replace-cross Abstract: Image registration is an ill-posed dense vision task, where multiple solutions achieve similar loss values, motivating probabilistic inference.
By Ivor J. A. Simpson, Neill D. F. Campbell
ReliMorph is a novel 3D deformable registration framework that integrates correspondence reliability with spatial expert routing and residual velocity gating. By using local correlation distributions to assess match ambiguity, the method guides expert fusion and velocity updates during coarse‑to‑fine refinement, with inverse‑inconsistency feedback refining decisions at finer levels. Experiments show that ReliMorph consistently improves anatomical overlap over competing methods in both unimodal and multimodal registration tasks.
By Chenyu Guo, Wenjie Dai, Yiqian Chang, Zhiyuan Zhang, Yunfan Wei, Shuhan Qi
UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.
By Zi Li, Jianpeng Zhang, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Zeli Chen, Xianghua Ye, Le Lu, Cheng Chen, Dakai Jin
The paper introduces Recursive Uncertainty-Gated Image Registration (RUGI), an iterative refinement method that updates deformation fields predicted by learning-based registration models using a gating map. Two gating strategies are explored: an uncertainty-based approach and an image residual error approach, both concentrating updates on difficult regions. Experiments on cardiac MRI and echocardiography datasets show that RUGI consistently improves registration accuracy, with the error-gated variant reducing MSE by 27‑37% on pretrained models and lowering ejection fraction estimation errors.
By Clara Rodrigo Gonz\'alez, Oscar Bates, Fu Siong Ng, Meng-Xing Tang
arXiv:2609.08627v1 Announce Type: cross
Abstract: In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, c...
By Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica
Multimodal image registration is a key component of many clinical workflows, yet it remains challenging because corresponding anatomical structures often exhibit substantially different image intensit...
arXiv:2610.34271v1 Announce Type: new
Abstract: We propose a novel method for robust point cloud registration with only one tunable parameter. Our method, dubbed TLP, uses truncated $L_p$ functions w...
By Seong Hun Lee, Patrick Vandewalle, Javier Civera
arXiv:2609.15669v1 Announce Type: cross
Abstract: Multimodal image registration is a key component of many clinical workflows, yet it remains challenging because corresponding anatomical structures o...
By Matteo Barbieri, Giammarco La Barbera, Juan Pablo De La Plata, Sabine Sarnacki, Isabelle Bloch, Pietro Gori
arXiv:2606. 27818v1 Announce Type: cross Abstract: We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points.
By Rixon Crane, Fahira Afzal Maken, Nicholas Lawrance, Stanislav Funiak, Kasra Khosoussi, Ming Xu, Russell Tsuchida
arXiv:2609.07460v1 Announce Type: cross
Abstract: Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in...
By Omar Todd, Sooha Kim, Raghav Mehta, Katherine Mackay, David Bernstein, Alexandra Taylor, Fabio De Sousa Ribeiro, Ben Glocker
DMM-Align introduces a closed‑loop framework for 2D‑3D registration that jointly refines correspondences, estimates pose, and learns representations using a shared differentiable geometric state. The method employs two diffusion processes: a geometry‑aware diffusion that improves the soft matching matrix for robust correspondence estimation, and a geometry‑conditioned diffusion teacher that feeds pose‑induced supervision back into feature learning. Experiments on 7‑Scenes and RGB‑D Scenes V2 show that DMM‑Align outperforms strong baselines, particularly in low‑overlap and heavily occluded scenarios, demonstrating the value of closed‑loop geometric feedback.
By Chongjian Wang, Junjie Gao