用随机游走匹配法对齐眼底图像，解决视角差异难题。
Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images
- 通过扩散模型引导的迭代随机游走搜索对应点
- 在真实临床数据上显著提升配准准确率
- 适合眼科多模态图像融合与疾病诊断应用
我们提出一种稳健的配准方法，用于对齐标准眼底图像（SFIs）与超广角眼底图像（UWFIs），二者因尺度、外观差异及特征稀缺而难以对齐。所提方法称为粒子扩散匹配（PDM），通过迭代随机游走对应搜索（RWCS）实现，由扩散模型引导。每轮迭代中，模型基于局部外观、粒子结构分布及全局变换估计，计算粒子点的位移向量，实现对应关系的逐步优化，即使在复杂条件下亦可有效工作。PDM在多个视网膜图像配准基准上达到当前最优性能，在主要的SFI-UWFI配对数据集上表现显著提升，并在真实临床场景中验证有效性。该方法提供精确且可扩展的对应估计，克服现有方法局限，促进互补性眼底成像模态的融合。此扩散引导的搜索策略为下游监督学习、疾病诊断及多模态图像分析提供了新方向。
原文摘要 · Abstract (English)
We propose a robust alignment technique for Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which are challenging to align due to differences in scale, appearance, and the scarcity of distinctive features. Our method, termed Particle Diffusion Matching (PDM), performs alignment through an iterative Random Walk Correspondence Search (RWCS) guided by a diffusion model. At each iteration, the model estimates displacement vectors for particle points by considering local appearance, the structural distribution of particles, and an estimated global transformation, enabling progressive refinement of correspondences even under difficult conditions. PDM achieves state-of-the-art performance across multiple retinal image alignment benchmarks, showing substantial improvement on a primary dataset of SFI-UWFI pairs and demonstrating its effectiveness in real-world clinical scenarios. By providing accurate and scalable correspondence estimation, PDM overcomes the limitations of existing methods and facilitates the integration of complementary retinal image modalities. This diffusion-guided search strategy offers a new direction for improving downstream supervised learning, disease diagnosis, and multi-modal image analysis in ophthalmology.
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