We introduce the RetinaRegNet model, which can achieve state-of-the-art performance across various retinal image registration tasks. RetinaRegNet does not require training on any retinal images. It begins by establishing point correspondences between two retinal images using image features derived from diffusion models. This process involves the selection of feature points from the moving image using the SIFT algorithm alongside random point sampling. For each selected feature point, a 2D correlation map is computed by assessing the similarity between the feature vector at that point and the feature vectors of all pixels in the fixed image. The pixel with the highest similarity score in the correlation map corresponds to the feature point in the moving image. To remove outliers in the estimated point correspondences, we first applied an inverse consistency constraint, followed by a transformation-based outlier detector. This method proved to outperform the widely used random sample consensus (RANSAC) outlier detector by a significant margin. To handle large deformations, we utilized a two-stage image registration framework. A homography transformation was used in the first stage and a more accurate third-order polynomial transformation was used in the second stage. The model's effectiveness was demonstrated across three retinal image datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. RetinaRegNet outperformed current state-of-the-art methods in all three datasets. It was especially effective for registering image pairs with large displacement and scaling deformations. This innovation holds promise for various applications in retinal image analysis. Our code is publicly available at https://github.com/mirthAI/RetinaRegNet .

翻译：我们提出了RetinaRegNet模型，该模型能够在各种视网膜图像配准任务中实现最先进的性能，且无需在任何视网膜图像上进行训练。该方法首先利用扩散模型提取的图像特征建立两幅视网膜图像之间的点对应关系。该过程包括使用SIFT算法结合随机点采样从移动图像中选取特征点，通过计算每个选定特征点的特征向量与固定图像中所有像素特征向量之间的相似性来生成二维相关性图，其中相关性图中相似性得分最高的像素即对应移动图像中的特征点。为去除估计点对应关系中的异常值，我们首先应用逆一致性约束，随后采用基于变换的异常值检测器。实验证明，该方法显著优于广泛使用的随机采样一致性（RANSAC）异常值检测器。为处理大形变，我们采用了两阶段图像配准框架：第一阶段使用单应性变换，第二阶段采用更精确的三阶多项式变换。该模型的有效性在三个视网膜图像数据集（彩色眼底图像、荧光素血管造影图像和激光散斑血流成像图像）上得到验证，在所有三个数据集中均优于当前最先进方法，尤其适用于具有大位移和尺度形变的图像对配准。这项创新为视网膜图像分析中的多种应用提供了可能。我们的代码已公开在https://github.com/mirthAI/RetinaRegNet。