论文标题
具有显着结构先验的深层视网膜图像质量评估网络
A Deep Retinal Image Quality Assessment Network with Salient Structure Priors
论文作者
论文摘要
视网膜图像质量评估是视网膜疾病诊断的重要先决条件。它的目标是确定视网膜图像，其中解剖结构和病变吸引眼科医生的注意力最清楚，绝对肯定地拒绝质量差的眼底图像。在此激励的过程中，我们模仿眼科医生评估视网膜图像质量的方式，并提出了一种称为Salsstructuiqa的方法。首先，两个用于自动视网膜质量评估的显着结构。一个是大尺寸的显着结构，包括光盘区域和大尺寸的渗出液。另一个是大小的显着结构，主要包括容器。然后，我们将提出的两个具有深卷积神经网络（CNN）的显着结构先验融合在一起，以将CNN的焦点转移到显着结构上。因此，我们开发了两个CNN体系结构：双分支Salsstructiqa和单分支Salstructiqa。双支分支Salstructiqa包含两个CNN分支，一个分支由大尺寸的显着结构进行指导，而另一个则由微小的显着结构进行指导。单分支Salinstructiqa包含一个CNN分支，该分支由大小和微型大小的显着结构的串联引导。眼睛质量数据集的实验结果表明，我们提出的双支支salstructiqa胜过视网膜图像质量评估和单支化的Salsstructiqa的最新方法，与最先进的深层视网膜图像质量评估方法相比，很轻巧，并且仍然很轻松。
Retinal image quality assessment is an essential prerequisite for diagnosis of retinal diseases. Its goal is to identify retinal images in which anatomic structures and lesions attracting ophthalmologists' attention most are exhibited clearly and definitely while reject poor quality fundus images. Motivated by this, we mimic the way that ophthalmologists assess the quality of retinal images and propose a method termed SalStructuIQA. First, two salient structures for automated retinal quality assessment. One is the large-size salient structures including optic disc region and exudates in large-size. The other is the tiny-size salient structures which mainly include vessels. Then we incorporate the proposed two salient structure priors with deep convolutional neural network (CNN) to shift the focus of CNN to salient structures. Accordingly, we develop two CNN architectures: Dual-branch SalStructIQA and Single-branch SalStructIQA. Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures. Single-branch SalStructIQA contains one CNN branch, which is guided by the concatenation of salient structures in both large-size and tiny-size. Experimental results on Eye-Quality dataset show that our proposed Dual-branch SalStructIQA outperforms the state-of-the-art methods for retinal image quality assessment and Single-branch SalStructIQA is much light-weight comparing with state-of-the-art deep retinal image quality assessment methods and still achieves competitive performances.
