U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

• DOI: 10.48550/arxiv.2004.03466
• Publication Date: 2020-01-01
• Authors: Wang, Shuhang; Hu, Szu-Yeu; Cheah, Eugene; Wang, Xiaohong; Wang, Jingchao; Chen, Lei; Baikpour, Masoud; Ozturk, Arinc; Li, Qian; Chou, Shinn-Huey; Lehman, Constance D.; Kumar, Viksit; Samir, Anthony
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Electrical Engineering and Systems Science - Image and Video Processing; Machine Learning (cs.LG)

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.<br/>8 pages MICCAI<br/>