  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 .
