  Segmentation of retinal vessel images is critical to the diagnosis of
retinopathy. Recently, convolutional neural networks have shown significant
ability to extract the blood vessel structure. However, it remains challenging
to refined segmentation for the capillaries and the edges of retinal vessels
due to thickness inconsistencies and blurry boundaries. In this paper, we
propose a novel deep neural network for retinal vessel segmentation based on
shared decoder and pyramid-like loss (SPNet) to address the above problems.
Specifically, we introduce a decoder-sharing mechanism to capture multi-scale
semantic information, where feature maps at diverse scales are decoded through
a sequence of weight-sharing decoder modules. Also, to strengthen
characterization on the capillaries and the edges of blood vessels, we define a
residual pyramid architecture which decomposes the spatial information in the
decoding phase. A pyramid-like loss function is designed to compensate possible
segmentation errors progressively. Experimental results on public benchmarks
show that the proposed method outperforms the backbone network and the
state-of-the-art methods, especially in the regions of the capillaries and the
vessel contours. In addition, performances on cross-datasets verify that SPNet
shows stronger generalization ability.
