Automatic Retinal Vessel Segmentation via Deeply Supervised and Smoothly Regularized Network

In recent years, retinal vessel segmentation technology has become an important component for disease screening and diagnosing in clinical medicine. However, retinal vessel segmentation is a challenging task due to complex distribution of blood vessels, relatively low contrast between target and background, and potential presence of illumination and pathologies. In this paper, we propose an automatic retinal vessel segmentation network using deep supervision and smoothness regularization, which integrates holistically-nested edge detector (HED) and global smoothness regularization from conditional random fields. It is an end-to-end and pixel-to-pixel deep convolutional network, can perform better results than HED-based methods and the methods where CRF inference is applied as a post-processing method. With co-constraints between pixels, the proposed DSSRN obtains better results. Finally, we show that our proposed method obtains the state-of-the-art vessel segmentation performance on all three benchmarks, DRIVE, STARE, and CHASE_DB1.