跨模态视网膜图像在大视野差异下的配准
Cross-modal Fundus Image Registration under Large FoV Disparity
AI总结：
本文提出CARe方法，用于解决大视野差异下的跨模态视网膜图像配准问题，通过裁剪和双拟合对齐改进配准效果。
摘要：
先前关于跨模态视网膜图像配准（CMFIR）的工作假设了小的跨模态视野（FoV）差异。相比之下，本文针对一个更具挑战性的场景，即大视野差异，而直接应用当前方法会失效。我们提出了Crop and Alignment for cross-modal fundus image Registration（CARe），一种非常简单但有效的方法。具体来说，给定一个视野较小的OCTA作为源图像和一个广视野彩色视网膜照片（wfCFP）作为目标图像，我们的Crop操作利用视网膜的生理结构，从目标图像中裁剪出一个其视野大致与源图像对齐的子图像。这一操作使我们能够重新利用之前针对小FoV差异的方法进行后续图像配准。此外，我们通过双拟合基于对齐模块改进空间变换，该模块利用经典RANSAC算法和基于多项式的坐标拟合按顺序进行。在新开发的包含60对OCTA-wfCFP的测试集上进行的广泛实验验证了CARe在CMFIR中的可行性。
英文摘要：
Previous work on cross-modal fundus image registration (CMFIR) assumes small cross-modal Field-of-View (FoV) disparity. By contrast, this paper is targeted at a more challenging scenario with large FoV disparity, to which directly applying current methods fails. We propose Crop and Alignment for cross-modal fundus image Registration(CARe), a very simple yet effective method. Specifically, given an OCTA with smaller FoV as a source image and a wide-field color fundus photograph (wfCFP) as a target image, our Crop operation exploits the physiological structure of the retina to crop from the target image a sub-image with its FoV roughly aligned with that of the source. This operation allows us to re-purpose the previous small-FoV-disparity oriented methods for subsequent image registration. Moreover, we improve spatial transformation by a double-fitting based Alignment module that utilizes the classical RANSAC algorithm and polynomial-based coordinate fitting in a sequential manner. Extensive experiments on a newly developed test set of 60 OCTA-wfCFP pairs verify the viability of CARe for CMFIR.
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。
