Romero-Bascones David, Barrenechea Maitane, Murueta-Goyena Ane, Galdós Marta, Gómez-Esteban Juan Carlos, Gabilondo Iñigo, Ayala Unai
Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, 20500 Mondragón, Spain.
Department of Preventive Medicine and Public Health, Faculty of Medicine and Nursery, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain.
Entropy (Basel). 2021 Jun 1;23(6):699. doi: 10.3390/e23060699.
Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.
理清产生人类视觉感知的细胞结构是眼科的主要挑战之一。特别令人感兴趣的是中央凹，它是位于视网膜中心的一个凹陷，捕捉来自注视中心的光线。近年来，通过从光学相干断层扫描（OCT）图像中提取几何特征来研究中央凹形态的兴趣日益浓厚。尽管如此，对于两个关键方法步骤的现有方法比较，研究关注较少：中央凹中心的定位和中央凹的数学建模。基于对185名健康受试者进行两次成像的数据集，本文首先研究了四种不同中央凹中心定位方法的图像配准精度。其次，从拟合误差、可重复性和偏差方面比较了当前最先进的中央凹数学模型。结果表明使用稳健的中央凹中心定位方法来对齐图像的重要性。此外，我们表明中央凹模型可以提高不同采集协议之间的一致性。然而，它们也可能在参数估计中引入应予以考虑的重要偏差。
