  Retinal vessels are important biomarkers for many ophthalmological and
cardiovascular diseases. Hence, it is of great significance to develop
automatic models for computer-aided diagnosis. Existing methods, such as U-Net
follow the encoder-decoder pipeline, where detailed information is lost in the
encoder in order to achieve a large field of view. Although spatial detailed
information could be recovered partly in the decoder, while there is noise in
the high-resolution feature maps of the encoder. And, we argue this
encoder-decoder architecture is inefficient for vessel segmentation. In this
paper, we present the detail-preserving network (DPN), which avoids the
encoder-decoder pipeline. To preserve detailed information and learn structural
information simultaneously, we designed the detail-preserving block (DP-Block).
Further, we stacked eight DP-Blocks together to form the DPN. More importantly,
there are no down-sampling operations among these blocks. Therefore, the DPN
could maintain a high/full resolution during processing, avoiding the loss of
detailed information. To illustrate the effectiveness of DPN, we conducted
experiments over three public datasets. Experimental results show, compared to
state-of-the-art methods, DPN shows competitive/better performance in terms of
segmentation accuracy, segmentation speed, and model size. Specifically, 1) Our
method achieves comparable segmentation performance on the DRIVE, CHASE_DB1,
and HRF datasets. 2) The segmentation speed of DPN is over 20-160 times faster
than other methods on the DRIVE dataset. 3) The number of parameters of DPN is1
around 120k, far less than all comparison methods.
