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 this https URL

RetinaRegNet 模型在不需要对任何视网膜图像进行训练的情况下，通过使用来自扩散模型的图像特征建立两个视网膜图像之间的点对应关系，实现了各种视网膜图像注册任务的最先进性能。该模型通过逆一致性约束和基于变换的异常点检测器去除估计的点对应关系中的异常值，并通过二阶图像配准和更精确的三阶多项式变换处理大变形，已在三个视网膜图像数据集上证明了其优越性能。

RetinaRegNet：视网膜图像注册的通用方法

RetinaRegNet: A Versatile Approach for Retinal Image Registration

Medical image registration is vital for disease diagnosis and treatment with
its ability to merge diverse information of images, which may be captured under
different times, angles, or modalities. Although several surveys have reviewed
the development of medical image registration, these surveys have not
systematically summarized methodologies of existing medical image registration
methods. To this end, we provide a comprehensive review of these methods from
traditional and deep learning-based directions, aiming to help audiences
understand the development of medical image registration quickly. In
particular, we review recent advances in retinal image registration at the end
of each section, which has not attracted much attention. Additionally, we also
discuss the current challenges of retinal image registration and provide
insights and prospects for future research.

医学图像配准对疾病诊断和治疗至关重要，能够合并不同时期、不同角度或不同模态下捕获的多样信息。本文系统综述了传统和基于深度学习的医学图像配准方法，并重点关注了视网膜图像配准的最新进展及其面临的挑战，同时提供未来研究的见解和展望。

医学图像配准及其在视网膜图像中的应用：一项综述

Medical Image Registration and Its Application in Retinal Images: A  Review

Retinal image registration is of utmost importance due to its wide
applications in medical practice. In this context, we propose ConKeD, a novel
deep learning approach to learn descriptors for retinal image registration. In
contrast to current registration methods, our approach employs a novel
multi-positive multi-negative contrastive learning strategy that enables the
utilization of additional information from the available training samples. This
makes it possible to learn high quality descriptors from limited training data.
To train and evaluate ConKeD, we combine these descriptors with domain-specific
keypoints, particularly blood vessel bifurcations and crossovers, that are
detected using a deep neural network. Our experimental results demonstrate the
benefits of the novel multi-positive multi-negative strategy, as it outperforms
the widely used triplet loss technique (single-positive and single-negative) as
well as the single-positive multi-negative alternative. Additionally, the
combination of ConKeD with the domain-specific keypoints produces comparable
results to the state-of-the-art methods for retinal image registration, while
offering important advantages such as avoiding pre-processing, utilizing fewer
training samples, and requiring fewer detected keypoints, among others.
Therefore, ConKeD shows a promising potential towards facilitating the
development and application of deep learning-based methods for retinal image
registration.

利用深度学习的方法，本研究提出了一种名为 ConKeD 的新型描述符，结合领域特定关键点实现视网膜图像配准，并通过新颖的多正面多负面对比学习策略，展示了比传统方法更好的性能，具有潜在的发展和应用前景。