AI 中文总结
针对现有视网膜配准方法依赖模态的局限，提出可泛化的两阶段模态无关框架，含稀疏特征匹配模型与MI-RAFT网络，在多模态视网膜图像配准中性能优于现有方法。
AI 中文摘要
视网膜图像配准对于眼科诊断、纵向疾病监测和多模态视网膜图像分析至关重要。现有视网膜配准方法通常依赖模态：它们被设计或优化用于单模态配准中的单一成像模态，或跨模态配准中的固定模态对，这限制了其在涉及多种视网膜成像模态及其不同组合的实际场景中的灵活性和适用性。本研究提出一种可泛化的两阶段模态无关视网膜图像配准框架：首先，引入由通用视网膜血管分割驱动的稀疏特征匹配模型，以实现跨模态的鲁棒粗粒度全局对齐；其次，开发名为MI-RAFT的模态无关光流估计网络，通过密集局部配准优化对齐。大量实验表明，该方法可处理常用视网膜成像模态的多样组合，展现出强模态无关性，且性能优于最先进的依赖模态的配准方法。
英文摘要
Retinal image registration is essential for ophthalmic diagnosis, longitudinal disease monitoring, and multimodal retinal image analysis. Existing retinal registration methods are typically modality-dependent: they are designed or optimized either for a single imaging modality in mono-modal registration or for a fixed pair of modalities in cross-modal registration. This limits their flexibility and applicability in practical scenarios involving diverse retinal imaging modalities and different combinations of them. In this work, we propose a generalizable two-stage, modality-invariant framework for retinal image registration. First, we introduce a sparse feature-matching model driven by a universal retinal vessel segmentation to achieve robust coarse global alignment across modalities. Second, we develop a modality-invariant optical flow estimation network, termed MI-RAFT, to refine the alignment through dense local registration. Extensive experiments demonstrate that the proposed method can handle diverse combinations of commonly used retinal imaging modalities, exhibiting strong modality invariance while outperforming state-of-the-art modality-dependent registration methods.
CommentsThis paper is a submission to IEEE Transactions on Image Processing (TIP-40498-2026)
