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Segmentation of retinal vessel images is critical to the diagnosis ofretinopathy. Recently, convolutional neural networks have shown significantability to extract the blood vessel structure. However, it remains challengingto refined segmentation for the capillaries and the edges of retinal vesselsdue to thickness inconsistencies and blurry boundaries. In this paper, wepropose a novel deep neural network for retinal vessel segmentation based onshared decoder and pyramid-like loss (SPNet) to address the above problems.Specifically, we introduce a decoder-sharing mechanism to capture multi-scalesemantic information, where feature maps at diverse scales are decoded througha sequence of weight-sharing decoder modules. Also, to strengthencharacterization on the capillaries and the edges of blood vessels, we define aresidual pyramid architecture which decomposes the spatial information in thedecoding phase. A pyramid-like loss function is designed to compensate possiblesegmentation errors progressively. Experimental results on public benchmarksshow that the proposed method outperforms the backbone network and thestate-of-the-art methods, especially in the regions of the capillaries and thevessel contours. In addition, performances on cross-datasets verify that SPNetshows stronger generalization ability.

SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss