解决大视场差异下的眼底图像跨模态配准难题
Cross-modal Fundus Image Registration under Large FoV Disparity
- 基于视网膜生理结构裁剪目标图像，对齐视场差异
- 在60对数据上实现93.3%的配准成功率，优于现有方法
- 适合临床医生与医学影像研究者使用
以往跨模态眼底图像配准（CMFIR）研究假设模态间视场（FoV）差异较小，而本文针对更具挑战性的大视场差异场景。提出一种简单有效的方法CARe：给定视场较小的OCTA作为源图像，宽视野彩色眼底照片（wfCFP）作为目标图像，通过裁剪操作利用视网膜生理结构提取与源图像视场大致对齐的目标子图，从而复用原有小视场差异方法。此外，设计基于RANSAC与多项式拟合的双阶段配准模块，提升空间变换精度。在新构建的60对OCTA-wfCFP测试集上，实验验证了该方法的有效性，配准成功率高达93.3%。
原文摘要 · Abstract (English)
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.
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