IEEE Trans Image Process. 2021;30:3167-3178. doi: 10.1109/TIP.2021.3058570. Epub 2021 Feb 25.
Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.
多模态视网膜成像是眼科学中一个重要的研究领域。本文提出了一种基于内容自适应的多模态视网膜图像配准方法，该方法侧重于全局粗略配准，并包含三个弱监督神经网络，用于血管分割、特征检测和描述以及异常值剔除。我们将所提出的框架应用于彩色眼底图像与红外反射和荧光血管造影图像的配准，并与几种传统和深度学习方法进行了比较。与其他方法相比，我们提出的框架在成功率和 Dice 系数等方面表现出更高的鲁棒性和准确性，取得了显著的改进。
