  Retinal vessel segmentation is an essential step for fundus image analysis.
With the recent advances of deep learning technologies, many convolutional
neural networks have been applied in this field, including the successful
U-Net. In this work, we firstly modify the U-Net with functional blocks aiming
to pursue higher performance. The absence of the expected performance boost
then lead us to dig into the opposite direction of shrinking the U-Net and
exploring the extreme conditions such that its segmentation performance is
maintained. Experiment series to simplify the network structure, reduce the
network size and restrict the training conditions are designed. Results show
that for retinal vessel segmentation on DRIVE database, U-Net does not
degenerate until surprisingly acute conditions: one level, one filter in
convolutional layers, and one training sample. This experimental discovery is
both counter-intuitive and worthwhile. Not only are the extremes of the U-Net
explored on a well-studied application, but also one intriguing warning is
raised for the research methodology which seeks for marginal performance
enhancement regardless of the resource cost.
