U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
This is an incremental improvement for medical image segmentation, offering better performance with reduced computational cost.
The paper tackled medical image segmentation by proposing SDU-Net, a U-Net variant using stacked dilated convolutions, which outperformed vanilla U-Net, AttU-Net, and R2U-Net in four tasks while using significantly fewer parameters (e.g., 40% of vanilla U-Net's).
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.