Study of Retinal Vessel Segmentation Algorithm Based on Receptive Field Expansion and Feature Refinement

Abstract

Missing blood vessels, fracturing blood vessels, and mistaking nonvascular features for blood vessels are major problems in retinal vessel segmentation tasks. This paper suggests an enhanced model that incorporates the Inception module and attention mechanism, based on the U-Net network topology, to solve these problems. In order to get richer scale information and enhance the model's recognition of vascular details, the encoder portion of the model first employs convolution kernels of varying sizes to collect multilevel characteristics of the picture. Second, to enhance feature processing between codecs and highlight significant features, an attention module is integrated into skip connections to extract spatial location information and interchannel interactions. This information is then coupled with residual connections. Finally, in the decoding stage, a residual attention module was constructed to extract vascular features and improve processing speed. On the DRIVE standard fundus image dataset, the proposed algorithm demonstrates significant performance enhancements compared to the conventional U-Net baseline. Specifically, it achieves absolute improvements of 1.94% in sensitivity, 1.07% in Jaccard index, 0.75% in Dice correlation coefficient, and 0.74% in Matthews correlation coefficient. Compared with other algorithms, it also has certain advantages and can effectively perform retinal vessel segmentation.