U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

• Main 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
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2020
• Subjects: Image and Video Processing eess.IV; Computer Vision and Pattern Recognition cs.CV; Machine Learning cs.LG; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; Attu
• Online Access: https://dx.doi.org/10.48550/arxiv.2004.03466; https://arxiv.org/abs/2004.03466

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. : 8 pages MICCAI