Retinal vessel segmentation method based on multi-scale dual-path convolutional neural network

Aiming to identify small blood vessels and low-contrast areas in retinal images, the paper presents an innovative approach to segmenting blood vessels in retinal images by employing a multi-scale dual-path convolutional neural network. It utilises Gabor filters to capture the unique characteristics of vessels at various scales, distinguishing between thick and thin vessels. The method integrates a dual-path network that employs convolution and sampling operations for advanced feature learning, leading to efficient end-to-end segmentation. The local vessel segmentation network features an encoder-decoder structure that retains spatial dimensions and employs dilated convolution to enhance the precision of thin vessel segmentation. A skip connection is added to further refine the segmentation of small vessels. The results on the DRIVE and CHASE_DB1 datasets show that this method outperforms existing techniques, achieving higher accuracy, sensitivity, and specificity. It successfully segments small, low-contrast vessels that are often overlooked while preserving the integrity and connectivity of the vascular structure.