A Dual-Path Deep Learning Architecture with Fusion Strategies and Timing for Retinal Vessel Segmentation

An accurate segmentation of retinal blood vessels is critical for the diagnosis and treatment of various ocular and systemic diseases. Over the past few years, deep learning-based models such as U-Net, SegNet, and HaarNet have demonstrated promising performance in medical image segmentation. However, single-path architectures often struggle with capturing fine vascular structures and balancing semantic and spatial features. Therefore, this study proposes a dual-path segmentation framework that integrates the strengths of the previous three models and investigates multiple fusion strategies, including element-wise addition and channel-wise concatenation followed by 1×1 convolution. A total of 12 dual-path models are constructed and evaluated on the DRIVE and STARE datasets. Experimental results show that the U-Net + HaarNet model using full-stage fusion with channel-wise concatenation achieves the best performance, with a Dice coefficient of 66.04%. All dual-path models significantly all outperforms three single-path models: HaarNet (49.09%), U-Net (52.94%), and SegNet (54.19%). These findings confirm that dual-path architectures with carefully designed fusion strategies can effectively enhance segmentation performance, particularly in capturing small and low-contrast retinal vessels.