通过对比无关实例优化适应冻结的单模背骨以实现多模注册
Adapting Frozen Mono-modal Backbones for Multi-modal Registration via Contrast-Agnostic Instance Optimization
AI总结：
本文提出一种结合冻结预训练单模注册模型与轻量级适应流程的多模图像注册框架，通过对比无关的表示生成与优化模块在测试时桥接模态与领域差距，提升注册鲁棒性。
摘要：
可变形图像注册仍然是医学图像分析中的核心挑战，尤其是在多模态场景中，不同扫描的强度分布差异显著。尽管深度学习方法提供高效的前馈预测，但它们在测试时面临分布偏移时往往无法稳健泛化。直接的全网络微调是一种简单的方法，但对于现代架构如Transformer或深度U-Net来说，在3D操作时内存和运行时间成本过高。同时，直接微调在存在剧烈领域偏移时性能可能下降。本文提出了一种注册框架，整合了冻结的预训练单模注册模型与轻量级适应流程用于多模态图像注册。具体而言，我们采用基于对比无关表示生成和优化模块的风格迁移技术，在测试时通过实例优化来弥合模态和领域差距。此设计与单模模型的选择无关，因此避免了全微调的计算负担，同时保持了适应新领域的能力。我们在Learn2Reg 2025 LUMIR验证集上评估了我们的方法，并观察到在预训练的最先进单模模型上的一致改进。特别是，该方法在多模态子集上排名第二，在领域外子集上排名第三，在Dice得分上获得第四名。这些结果表明，结合冻结单模模型与模态适应和轻量级实例优化提供了一条有效且实用的路径，以实现稳健的多模态注册。
英文摘要：
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 \textbf{mono-modal} registration model with a lightweight adaptation pipeline for \textbf{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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
