Nankai University Eye Institute, Nankai University, Tianjin 300350, China; Institute of Robotics and Automatic Information System (IRAIS), the Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Nankai University, Tianjin 300350, China.
Nankai University Eye Institute, Nankai University, Tianjin 300350, China; Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin Medical University, Tianjin 300350, China.
Med Image Anal. 2024 Oct;97:103242. doi: 10.1016/j.media.2024.103242. Epub 2024 Jun 15.
The development of myopia is usually accompanied by changes in retinal vessels, optic disc, optic cup, fovea, and other retinal structures as well as the length of the ocular axis. And the accurate registration of retinal images is very important for the extraction and analysis of retinal structural changes. However, the registration of retinal images with myopia development faces a series of challenges, due to the unique curved surface of the retina, as well as the changes in fundus curvature caused by ocular axis elongation. Therefore, our goal is to improve the registration accuracy of the retinal images with myopia development.
In this study, we propose a 3D spatial model for the pair of retinal images with myopia development. In this model, we introduce a novel myopia development model that simulates the changes in the length of ocular axis and fundus curvature due to the development of myopia. We also consider the distortion model of the fundus camera during the imaging process. Based on the 3D spatial model, we further implement a registration framework, which utilizes corresponding points in the pair of retinal images to achieve registration in the way of 3D pose estimation.
The proposed method is quantitatively evaluated on the publicly available dataset without myopia development and our Fundus Image Myopia Development (FIMD) dataset. The proposed method is shown to perform more accurate and stable registration than state-of-the-art methods, especially for retinal images with myopia development.
To the best of our knowledge, this is the first retinal image registration method for the study of myopia development. This method significantly improves the registration accuracy of retinal images which have myopia development. The FIMD dataset we constructed has been made publicly available to promote the study in related fields.
近视的发展通常伴随着视网膜血管、视盘、视杯、黄斑等视网膜结构以及眼球轴长的变化。准确地对视网膜图像进行配准对于提取和分析视网膜结构变化非常重要。然而，由于视网膜的独特曲面以及眼球轴伸长引起的眼底曲率变化，近视发展的视网膜图像配准面临一系列挑战。因此，我们的目标是提高近视发展的视网膜图像配准精度。
在这项研究中，我们提出了一种用于近视发展的视网膜图像对的三维空间模型。在该模型中，我们引入了一种新的近视发展模型，模拟了由于近视发展而引起的眼球轴长和眼底曲率的变化。我们还考虑了眼底相机在成像过程中的失真模型。基于三维空间模型，我们进一步实现了一个注册框架，该框架利用对视网膜图像对中的对应点进行三维位姿估计来实现注册。
所提出的方法在没有近视发展的公开数据集和我们的眼底图像近视发展（FIMD）数据集上进行了定量评估。与最先进的方法相比，所提出的方法表现出更准确和更稳定的配准，特别是对于具有近视发展的视网膜图像。
据我们所知，这是第一个用于研究近视发展的视网膜图像配准方法。该方法显著提高了具有近视发展的视网膜图像的配准精度。我们构建的 FIMD 数据集已公开发布，以促进相关领域的研究。
