基于全局和局部可变形变换的渐进式视网膜图像配准
Progressive Retinal Image Registration via Global and Local Deformable Transformations
AI总结：
针对视网膜曲面导致平面假设产生配准误差的问题，提出混合框架HybridRetina，通过关键点检测器和形变网络GAMorph结合边缘注意力机制，渐进式实现全局与局部可变形变换，在FIRE和FLoRI21数据集上显著优于现有方法。
摘要：
视网膜图像配准在眼科诊断过程中扮演着重要角色。由于不同视网膜图像在视角和解剖结构上存在差异，基于关键点的方法凭借其鲁棒性和低延迟性成为视网膜图像配准的主流方法。这些方法通常假设视网膜表面是平面的，并采用特征匹配来获取表示图像间全局变换的单应性矩阵。然而，由于视网膜表面近似于曲面，这种平面假设不可避免地会引入配准误差。当配准视角差异显著的图像对时，这一局限性更加突出。为了解决这个问题，我们提出了一种名为HybridRetina的混合配准框架，该框架通过全局和局部可变形变换渐进地配准视网膜图像。为此，我们使用关键点检测器和名为GAMorph的形变网络，分别估计全局变换和局部可变形变换。具体而言，我们整合了多级像素关系知识来指导GAMorph的训练。此外，我们利用一个包含图像几何先验的边缘注意力模块，确保形变场更聚焦于具有临床价值的血管区域。在两个广泛使用的数据集FIRE和FLoRI21上的实验表明，我们提出的HybridRetina显著优于一些最先进的方法。代码可在https://github.com/lyp-deeplearning/awesome-retinal-registration获取。
英文摘要：
Retinal image registration plays an important role in the ophthalmological diagnosis process. Since there exist variances in viewing angles and anatomical structures across different retinal images, keypoint-based approaches become the mainstream methods for retinal image registration thanks to their robustness and low latency. These methods typically assume the retinal surfaces are planar, and adopt feature matching to obtain the homography matrix that represents the global transformation between images. Yet, such a planar hypothesis inevitably introduces registration errors since retinal surface is approximately curved. This limitation is more prominent when registering image pairs with significant differences in viewing angles. To address this problem, we propose a hybrid registration framework called HybridRetina, which progressively registers retinal images with global and local deformable transformations. For that, we use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation, respectively. Specifically, we integrate multi-level pixel relation knowledge to guide the training of GAMorph. Additionally, we utilize an edge attention module that includes the geometric priors of the images, ensuring the deformation field focuses more on the vascular regions of clinical interest. Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods. The code is available at https://github.com/lyp-deeplearning/awesome-retinal-registration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
