  Retinal blood vessel can assist doctors in diagnosis of eye-related diseases
such as diabetes and hypertension, and its segmentation is particularly
important for automatic retinal image analysis. However, it is challenging to
segment these vessels structures, especially the thin capillaries from the
color retinal image due to low contrast and ambiguousness. In this paper, we
propose pyramid U-Net for accurate retinal vessel segmentation. In pyramid
U-Net, the proposed pyramid-scale aggregation blocks (PSABs) are employed in
both the encoder and decoder to aggregate features at higher, current and lower
levels. In this way, coarse-to-fine context information is shared and
aggregated in each block thus to improve the location of capillaries. To
further improve performance, two optimizations including pyramid inputs
enhancement and deep pyramid supervision are applied to PSABs in the encoder
and decoder, respectively. For PSABs in the encoder, scaled input images are
added as extra inputs. While for PSABs in the decoder, scaled intermediate
outputs are supervised by the scaled segmentation labels. Extensive evaluations
show that our pyramid U-Net outperforms the current state-of-the-art methods on
the public DRIVE and CHASE-DB1 datasets.
