Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4086-4091. doi: 10.1109/EMBC46164.2021.9631084.
Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.
多模态视网膜图像配准在 2D 超广角 (UWF) 和窄角 (NA) 图像之间尚未得到很好的研究，因为大多数现有方法主要集中在 NA 图像配准上。UWF 成像中使用的球面投影模型在外周区域会产生强烈的变形，从而导致配准质量下降。我们提出了一种基于估计 NA 图像相机视点的失真校正方法来重映射 UWF 图像。此外，我们为 UWF 和 NA 图像建立了一个基于 CNN 的注册流水线，它包括失真校正方法和三个用于血管分割、特征检测和匹配以及异常值剔除的网络。在我们收集的数据集上的实验结果表明了所提出的流水线和失真校正方法的有效性。
