SDDC-Net: A U-shaped deep spiking neural P convolutional network for retinal vessel segmentation

Abstract

As an essential step in the early diagnosis of retinopathy, the blood vessels morphological attributes assist specialists to obtain pathological information efficiently. Most existing deep learning methods are based on U-shaped convolutional neural networks for the segmentation of blood vessels and have made substantial progress. However, the variance between vessel images remains challenging for segmentation algorithms, as demonstrated by poor cross-validation performance between different datasets. In this paper, a novel U-shaped deep convolutional network is proposed for retinal vessel segmentation, namely spiking neural P-type Dual-channel dilated convolutional network (SDDC-Net). We redesign the classical U-shaped convolution network based on the spiking neural P system computational mechanism for the first time. Distinct from the conventional convolutional neural network, SDDC-Net integrates the spiking neural P system convolutional neurons into the classic encoder-decoder architecture. We employ dilated convolution into an encoder, which improves both capabilities of perceiving more contexts and perceptual sensitivity of thin blood. We evaluate this model on three public datasets (DRIVE, STARE, CHASE_DB1), which indicates the more sensitive detection of thin vessels compared to most existing methods, showing higher sensitivity and F1 metrics. Compared to the baseline U-Net, our sensitivity, accuracy, and F1 score metrics on DRIVE dataset surpass by 10.66%, 1.73%, and 1.47% respectively. We also evaluate the effectiveness of spiking neural P system and dilated convolution in the ablation experiments, which demonstrates that accuracy increases with few drops in specificity. The cross-validation experiments show that our model has not only effective segmentation ability but also excellent generalization ability.