  This paper proposes a novel U-Net variant using stacked dilated convolutions
for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of
vanilla U-Net with modifications in the encoder and decoder operations (an
operation indicates all the processing for feature maps of the same
resolution). Unlike vanilla U-Net which incorporates two standard convolutions
in each encoder/decoder operation, SDU-Net uses one standard convolution
followed by multiple dilated convolutions and concatenates all dilated
convolution outputs as input to the next operation. Experiments showed that
SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent
residual U-Net (R2U-Net) in all four tested segmentation tasks while using
parameters around 40% of vanilla U-Net's, 17% of AttU-Net's, and 15% of
R2U-Net's.
