Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., `Accept' and `Reject'). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., `Good', `Usable' and `Reject') for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.

本研究使用三级分级系统对一个由28792张来自EyePACS数据集的视网膜图像组成的眼睛质量（EyeQ）数据集进行了重新注释，然后分析了不同色彩空间对视网膜图像质量评估的影响，并提出了一种名为多色彩空间融合网络(MCF-Net)的简单而有效的深度网络，该网络将不同色彩空间表示集成到特征级和预测级来预测图像质量分级。实验证明，MCF-Net比其他深度学习方法表现更好，并且还评估了不同质量图像上自动诊断系统的性能表现。

评估不同色彩空间中的视网膜图像质量评估网络