A Deep Retinal Image Quality Assessment Network with Salient Structure Priors
This work is significant for ophthalmologists and automated diagnostic systems, as it improves the accuracy and efficiency of identifying high-quality retinal images for disease diagnosis.
This paper addresses the problem of retinal image quality assessment, which is crucial for diagnosing retinal diseases. The authors propose SalStructuIQA, a method that incorporates salient structure priors into deep convolutional neural networks. Their Dual-branch SalStructIQA outperforms state-of-the-art methods on the Eye-Quality dataset, while the Single-branch SalStructuIQA offers competitive performance with a lighter-weight architecture.
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.