RetinaRegNet：一种用于视网膜图像配准的零样本方法
RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration
AI总结：
本研究提出零样本视网膜图像配准模型RetinaRegNet，结合扩散特征提取、逆一致性外点剔除与两阶段配准框架，在三类视网膜图像数据集上均优于现有最优方法，可辅助眼部疾病诊疗相关应用。
摘要：
我们提出了RetinaRegNet，这是一款零样本图像配准模型，旨在配准重叠度极低、形变量大且图像质量参差不齐的视网膜图像。RetinaRegNet可应对上述挑战，通过以下步骤实现鲁棒且精准的配准。首先，我们利用潜在扩散模型从浮动图像和固定图像中提取特征。随后，结合SIFT（尺度不变特征变换）算法与随机点采样方法，从固定图像中采样特征点。对于每个采样点，我们通过二维相关图确定其在浮动图像中的对应点，该相关图用于计算固定图像中该点的扩散特征向量与浮动图像中所有像素的扩散特征向量之间的余弦相似度。其次，我们通过施加逆一致性约束消除大部分错误检测的点对应关系（即外点），确保对应关系在正向和反向方向上均保持一致。我们还采用基于全局变换的外点检测器，进一步移除对应点间距过大的外点。最后，我们构建了一个两阶段配准框架以应对大形变问题：第一阶段估计单应性变换，实现图像间的全局对齐；第二阶段使用三阶多项式变换估计局部形变。我们在三个视网膜图像配准数据集上对RetinaRegNet进行了评估，分别是彩色眼底图像数据集、荧光素血管造影图像数据集和激光散斑血流成像图像数据集。我们的模型在所有数据集上的表现均始终优于现有最优方法。RetinaRegNet实现的精准配准可用于追踪眼部疾病进展、优化手术规划以及辅助评估治疗效果。我们的代码已公开发布于：https://github.com/mirthAI/RetinaRegNet。
英文摘要：
We introduce RetinaRegNet, a zero-shot image registration model designed to register retinal images with minimal overlap, large deformations, and varying image quality. RetinaRegNet addresses these challenges and achieves robust and accurate registration through the following steps. First, we extract features from the moving and fixed images using latent diffusion models. We then sample feature points from the fixed image using a combination of the SIFT algorithm and random point sampling. For each sampled point, we identify its corresponding point in the moving image using a 2D correlation map, which computes the cosine similarity between the diffusion feature vectors of the point in the fixed image and all pixels in the moving image. Second, we eliminate most incorrectly detected point correspondences (outliers) by enforcing an inverse consistency constraint, ensuring that correspondences are consistent in both forward and backward directions. We further remove outliers with large distances between corresponding points using a global transformation based outlier detector. Finally, we implement a two-stage registration framework to handle large deformations. The first stage estimates a homography transformation to achieve global alignment between the images, while the second stage uses a third-order polynomial transformation to estimate local deformations. We evaluated RetinaRegNet on three retinal image registration datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. Our model consistently outperformed state-of-the-art methods across all datasets. The accurate registration achieved by RetinaRegNet enables the tracking of eye disease progression, enhances surgical planning, and facilitates the evaluation of treatment efficacy. Our code is publicly available at: https://github.com/mirthAI/RetinaRegNet.
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