Retinal Image Quality Assessment via Specific Structures Segmentation

Abstract

Quality of retinal image plays an essential role in ophthalmic disease diagnosis. However, most of the existing models neglect the potential correlation between retinal structure segmentation and retinal image quality assessment (RIQA), since the segmentation result is able to provide the region of interests (ROIs) and the RIQA model can extract more discriminative features. Therefore, in this paper, we incorporate the retinal structure segmentation process into RIQA tasks and thus propose a structure-guided deep neural network (SG-Net) for better image quality assessment. The SG-Net consists of a vessel segmentation module, an optic disc segmentation module, and a quality assessment module. The vessel segmentation module and optic disk segmentation module generate the segmentation results of important retinal structures (i.e., vessel and optic disc) that provide supplementary knowledge to support the quality assessment module. The quality assessment module is a three-branches classification network to extract and fuse features to estimate image quality. We evaluated our proposed SG-Net on the Eye-Quality (EyeQ) database, and the experiment results demonstrated that the proposed SG-Net outperforms other existing state-of-the-art methods. Our ablation studies also indicated that each structure segmentation module is able to achieve impressive performance gain on the EyeQ database.