Dual Encoding U-Net for Retinal Vessel Segmentation

Retinal Vessel Segmentation is an essential step for the early diagnosis of eye-related diseases, such as diabetes and hypertension. Segmentation of blood vessels requires both sizeable receptive field and rich spatial information. In this paper, we propose a novel Dual Encoding U-Net (DEU-Net), which have two encoders: a spatial path with large kernel to preserve the spatial information and a context path with multiscale convolution block to capture more semantic information. On the top of the two paths, we introduce a feature fusion module to combine the different level of feature representation. Besides, we apply channel attention to select useful feature map in a skip connection. Furthermore, low-level and high-level prediction are combined in multiscale prediction module for a better accuracy. We evaluated this model on the digital retinal images for vessel extraction (DRIVE) dataset and the child heart and health study (CHASEDB1) dataset. Results show that the proposed DEU-Net model achieved the state-of-the-art retinal vessel segmentation accuracy on both datasets.