粒子扩散匹配：用于标准和超宽场视网膜图像对齐的随机游走对应搜索
Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images
AI总结：
本文提出了一种稳健的对齐技术，通过扩散模型引导的迭代随机游走对应搜索（RWCS）实现标准视网膜图像（SFIs）和超宽场视网膜图像（UWFIs）的对齐，解决了尺度、外观差异和特征稀少的问题，展示了在多个视网膜图像对齐基准上的最佳性能。
摘要：
我们提出了一种稳健的对齐技术用于标准视网膜图像（SFIs）和超宽场视网膜图像（UWFIs），这些图像由于尺度、外观差异以及显著特征稀缺而难以对齐。我们的方法称为粒子扩散匹配（PDM），通过由扩散模型引导的迭代随机游走对应搜索（RWCS）进行对齐。在每次迭代中，模型通过考虑局部外观、粒子的结构分布以及估计的全局变换来估计粒子点的位移向量，从而在困难条件下逐步优化对应关系。PDM在多个视网膜图像对齐基准上实现了最先进的性能，显示出在SFI-UWFI配对数据集上的显著改进，并在真实世界临床场景中证明了其有效性。通过提供准确且可扩展的对应关系估计，PDM克服了现有方法的局限性，促进了互补视网膜图像模态的整合。这种扩散引导的搜索策略为眼科领域下游监督学习、疾病诊断和多模态图像分析的改进提供了新方向。
英文摘要：
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.
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。
