Semantic Segmentation of Retinal Vessel Images via Dense Convolution and Depth Separable Convolution

Semantic segmentation of retinal vessel images is of great value for clinical diagnosis. Due to the complex information of retinal vessel features, the existing algorithms have problems such as discontinuities of segmented vessels. To achieve better semantic segmentation results, we propose an encoder-decoder structure combined with dense convolution and depth separable convolution. Firstly, the images are enhanced by extracting the original green channel, limiting contrast histogram equalization and sharpening, then data argumentation is performed to expand the data set. Secondly, the processed images are trained by the proposed network using a weighted loss function. Finally, the test images are segmented by the trained model. The proposed algorithm is tested on the DRIVE data set, and its average accuracy, sensitivity and specificity reached 96.83%, 83.71%, and 98.95%, respectively.