Vessel Segmentation and Dirt/Reflection Detection For Retinal Fundus Photographs

We propose an adversarial training framework to simultaneously address the vessel segmentation and dirt/reflection removal problems in fundus photographs used for diabetic retinopathy diagnosis. This framework contains two primary subnetworks, each triggered by a set of loss terms, i.e., one for segmentation and the other for reconstruction. These two subnetworks act as inverse functions of each other so that they form an autoencoder framework with a 2-dimensional latent code, which can be a vessel segmentation mask after binarization. To further improve the segmentation and reconstruction performance, we devise a loss function based on gradient vector flow (GVF) and reorganize the generator network. Experimental results show that the proposed method has a good generalization capability. Trained on DRIVE’s training set, our model can produce segmentation and reconstruction-based artifact removal results stably on other datasets like CHASE-DB1 and STARE. The average F1-score of our segmentation results of DRIVE’s testing set reaches 0.7964, and the artifact-free reconstruction results can achieve an average PSNR of 24 dB.