OCT Image Quality Evaluation Based on Deep and Shallow Features Fusion Network

Abstract

Optical coherence tomography (OCT) has become an important tool for the diagnosis of retinal diseases, and image quality assessment on OCT images has considerable clinical significance for guaranteeing the accuracy of diagnosis by ophthalmologists. Traditional OCT image quality assessment is usually based on hand-crafted features including signal strength index and signal to noise ratio. These features only reflect a part of image quality, but cannot be seen as a full representation on image quality. Especially, there is no detailed description of OCT image quality so far. In this paper, we firstly define OCT image quality as three grades (‘Good’, ‘Usable’ and ‘Poor’). Considering the diversity of image quality, we then propose a deep and shallow features fusion network (DSFF-Net) to conduct multiple label classification. The DSFF-Net combines deep and enhanced shallow features of OCT images to predict the image quality grade. The experimental results on a large OCT dataset show that our network obtains state-of-the-art performance, outperforming the other classical CNN networks.