U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

TL;DR

This paper introduces SDU-Net, a novel medical image segmentation model that enhances the U-Net architecture with stacked dilated convolutions, achieving superior performance with fewer parameters.

Contribution

The paper presents a new U-Net variant using stacked dilated convolutions, improving segmentation accuracy and parameter efficiency over existing models.

Findings

SDU-Net outperforms vanilla U-Net, AttU-Net, and R2U-Net in segmentation tasks.

SDU-Net uses significantly fewer parameters, around 15-40% of compared models.

Experimental results demonstrate improved segmentation accuracy across four tasks.

Abstract

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.