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

• URL: http://arxiv.org/abs/2604.09743v1
• Date: Fri, 10 Apr 2026 00:42:24 GMT
• Title: Search-MIND: Training-Free Multi-Modal Medical Image Registration
• Abstract summary: We propose Search-MIND, a training-free, iterative optimization framework for instance-specific registration.<n>We introduce two novel loss functions: Variance-Weighted Mutual Information (VWMI), which prioritizes informative tissue regions, and Search-MIND (S-MIND), which broadens the convergence basin of structural descriptors.<n> Evaluations on CARE Liver 2025 and CHAOS Challenge datasets show that Search-MIND consistently outperforms classical baselines.
• Score: 13.33205970973723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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