# U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2004.03466 · HTML: https://mlsanity.com/paper/2004.03466/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Hyper-Convolution Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.10559/) (2021)
- [SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation](https://mlsanity.com/paper/2309.16661/) (2023)
- [Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention](https://mlsanity.com/paper/2506.18335/) (2025)
- [Hyper-Convolutions via Implicit Kernels for Medical Imaging](https://mlsanity.com/paper/2202.02701/) (2022)
- [Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion](https://mlsanity.com/paper/2504.06158/) (2025)
- [Network-Agnostic Knowledge Transfer for Medical Image Segmentation](https://mlsanity.com/paper/2101.09560/) (2021)
- [A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation](https://mlsanity.com/paper/2505.15802/) (2025)
- [InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation](https://mlsanity.com/paper/2506.12208/) (2025)
- [3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases](https://mlsanity.com/paper/2604.28064/) (2026)

## Abstract (arXiv)

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.