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：摘要：我们提出了RetinaRegNet模型，该模型能够在多种视网膜图像配准任务中实现最先进的性能。RetinaRegNet无需在任何视网膜图像上进行训练。它首先利用扩散模型提取的图像特征，在两幅视网膜图像之间建立点对应关系。此过程涉及使用SIFT算法从移动图像中选择特征点，并结合随机点采样。对于每个选定的特征点，通过计算该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，生成一个二维相关图。相关图中最高相似性得分的像素对应于移动图像中的特征点。为去除估计点对应中的离群点，我们首先应用逆一致性约束，随后采用基于变换的离群点检测器。该方法被证明显著优于广泛使用的随机采样一致性（RANSAC）离群点检测器。为处理大形变，我们利用了两阶段图像配准框架：第一阶段使用单应性变换，第二阶段使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上得到验证：彩色眼底图像、荧光素血管造影图像和激光散斑血流图图像。RetinaRegNet在所有三个数据集上均优于当前最先进方法，尤其适用于具有大位移和缩放形变的图像对配准。这项创新为视网膜图像分析的多种应用提供了前景。我们的代码公开于https://github.com/mirthAI/RetinaRegNet。