Search-MIND: Training-Free Multi-Modal Medical Image Registration

Boya Wang; Ruizhe Li; Chao Chen; Xin Chen

eess.IVApr 10, 2026

Search-MIND: Training-Free Multi-Modal Medical Image Registration

Authors: Boya Wang, Ruizhe Li, Chao Chen, Xin Chen

Organizations: Intelligent Modelling & Analysis Group (IMA), School of Computer Science, University of Nottingham, UK · Lab for Uncertainty in Data and Decision Making (LUCID), Nottingham Biomedical Research Centre (BRC), School of Medicine, University of Nottingham, UK

Abstract

Multi-modal image registration plays a critical role in precision medicine but faces challenges from non-linear intensity relationships and local optima. While deep learning models enable rapid inference, they often suffer from generalization collapse on unseen modalities. To address this, we propose Search-MIND, a training-free, iterative optimization framework for instance-specific registration. Our pipeline utilizes a coarse-to-fine strategy: a hierarchical coarse alignment stage followed by deformable refinement. We introduce two novel loss functions: Variance-Weighted Mutual Information (VWMI), which prioritizes informative tissue regions to shield global alignment from background noise and uniform regions, and Search-MIND (S-MIND), which broadens the convergence basin of structural descriptors by considering larger local search range. Evaluations on CARE Liver 2025 and CHAOS Challenge datasets show that Search-MIND consistently outperforms classical baselines like ANTs and foundation model-based approaches like DINO-reg, offering superior stability across diverse modalities.

Figures & tables

Figure 1: Overview of the proposed registration framework.

Method	T1			T2			DWI			T(s) $\downarrow$
	DSC $\uparrow$	$J\!\leq\!0$ $\downarrow$	$\sigma(\log J)$ $\downarrow$	DSC $\uparrow$	$J\!\leq\!0$ $\downarrow$	$\sigma(\log J)$ $\downarrow$	DSC $\uparrow$	$J\!\leq\!0$ $\downarrow$	$\sigma(\log J)$ $\downarrow$	T(s) $\downarrow$
Initial	0.818	–	–	0.642	–	–	0.666	–	–	–
ANTs-Rigid	0.879	–	–	0.703	–	–	0.705	–	–	10.0
Ours (R-MI)	0.870	–	–	0.646 †	–	–	0.703 †	–	–	5.4
Ours (R-VWMI)	0.871	–	–	0.710	–	–	0.725	–	–	4.8
ANTs-SyN	0.908*	0.000	0.223	0.601*	0.000	0.274	0.672*	0.000	0.300	55.2

Table 1: Quantitative results on the CARE dataset. Methods are grouped into pre-alignment (Initial and after coarse alignment), classical deformable baselines (ANTs-SyN, DINO-reg), and the proposed approaches.

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Adapting Frozen Mono-modal Backbones for Multi-modal Registration via Contrast-Agnostic Instance Optimization

Yi Zhang, Yidong Zhao, Qian Tao

Department of Imaging Physics, Delft University of Technology, The Netherlands

Deformable Image Registration Cross-Modal Image Registration Test-Time Adaptation

Open paper AskPDF arXiv

Deformable image registration remains a central challenge in medical image analysis, particularly under multi-modal scenarios where intensity distributions vary significantly across scans. While deep learning methods provide efficient feed-forward predictions, they often fail to generalize robustly under distribution shifts at test time. A straightforward remedy is full network fine-tuning, yet for modern architectures such as Transformers or deep U-Nets, this adaptation is prohibitively expensive in both memory and runtime when operating in 3D. Meanwhile, the naive fine-tuning struggles more with potential degradation in performance in the existence of drastic domain shifts. In this work, we propose a registration framework that integrates a frozen pretrained mono-modal registration model with a lightweight adaptation pipeline for multi-modal image registration. Specifically, we employ style transfer based on contrast-agnostic representation generation and refinement modules to bridge modality and domain gaps with instance optimization at test time. This design is orthogonal to the choice of backbone mono-modal model, thus avoids the computational burden of full fine-tuning while retaining the flexibility to adapt to unseen domains. We evaluate our approach on the Learn2Reg 2025 LUMIR validation set and observe consistent improvements over the pretrained state-of-the-art mono-modal backbone. In particular, the method ranks second on the multi-modal subset, third on the out-of-domain subset, and achieves fourth place overall in Dice score. These results demonstrate that combining frozen mono-modal models with modality adaptation and lightweight instance optimization offers an effective and practical pathway toward robust multi-modal registration.

Abstract

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