Exploiting Residual Edge Information in Deep Fully Convolutional Neural Networks For Retinal Vessel Segmentation

Accurate automatic segmentation of the retinal vessels is crucial for early detection and diagnosis of vision-threatening retinal diseases. A new supervised method using a variant of the fully convolutional neural network is pro-posed with the advantages of reduced hyper-parameters, reduced computational/memory requirements, and robust performance in capturing tiny vessel information. The fully convolutional architectures previously employed for vessel segmentation have multiple tunable hyperparameters and difficulty in end-to-end training due to their decoder structure. We resolve this problem by sharing information from the encoder for upsampling at the decoder stage, resulting in a significantly smaller number of tunable parameters and low computational overhead at the train and test stages. Moreover, the need for pre- and post-processing steps are eradicated. Consequently, the detection accuracy is significantly improved with scores of 0.9620, 0.9623, and 0.9620 on DRIVE, STARE, and CHASE_DB1 datasets respectively.