# DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation

arXiv: https://arxiv.org/abs/2006.00414 · HTML: https://mlsanity.com/paper/2006.00414/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 28 of 28)

- [Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2303.16892/) (2023)
- [Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation](https://mlsanity.com/paper/2203.15177/) (2022)
- [G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation](https://mlsanity.com/paper/2310.16175/) (2023)
- [MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder](https://mlsanity.com/paper/2310.19898/) (2023)
- [CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation](https://mlsanity.com/paper/2105.04075/) (2021)
- [Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2](https://mlsanity.com/paper/2408.01648/) (2024)
- [NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping](https://mlsanity.com/paper/2410.05341/) (2024)
- [WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters](https://mlsanity.com/paper/2308.11776/) (2023)
- [SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)](https://mlsanity.com/paper/2308.11774/) (2023)
- [Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image](https://mlsanity.com/paper/2211.14467/) (2022)
- [Leveraging sinusoidal representation networks to predict fMRI signals from EEG](https://mlsanity.com/paper/2311.04234/) (2023)
- [Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models](https://mlsanity.com/paper/2410.07434/) (2024)
- [Medical Image Segmentation via Sparse Coding Decoder](https://mlsanity.com/paper/2310.10957/) (2023)
- [GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs](https://mlsanity.com/paper/2502.11731/) (2025)
- [DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner](https://mlsanity.com/paper/2304.09620/) (2023)
- [Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2310.18656/) (2023)
- [Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation](https://mlsanity.com/paper/2510.08949/) (2025)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation](https://mlsanity.com/paper/2207.11446/) (2022)
- [MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images](https://mlsanity.com/paper/2210.12361/) (2022)
- [WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT](https://mlsanity.com/paper/2211.07143/) (2022)
- [Dual skip connections in U-Net, ResUnet and U-Net3+ for remote extraction of buildings](https://mlsanity.com/paper/2303.09064/) (2023)
- [Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer](https://mlsanity.com/paper/2404.11361/) (2024)
- [Modifying the U-Net's Encoder-Decoder Architecture for Segmentation of Tumors in Breast Ultrasound Images](https://mlsanity.com/paper/2409.00647/) (2024)
- [KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation](https://mlsanity.com/paper/2409.20414/) (2024)
- [DaRePlane: Direction-aware Representations for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2410.14169/) (2024)
- [Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement](https://mlsanity.com/paper/2505.01831/) (2025)
- [Breast Cell Segmentation Under Extreme Data Constraints: Quantum Enhancement Meets Adaptive Loss Stabilization](https://mlsanity.com/paper/2512.02302/) (2025)

## Abstract (arXiv)

Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images. In this regard, U-Net is the predominant approach to medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some tough cases of them. However, we found that the classical U-Net architecture has limitation in several aspects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder and decoder, 2) applied residual module to replace skip connection between encoder and decoder to improve based on the-state-of-the-art U-Net model. Following these modifications, we designed a novel architecture--DC-UNet, as a potential successor to the U-Net architecture. We created a new effective CNN architecture and build the DC-UNet based on this CNN. We have evaluated our model on three datasets with tough cases and have obtained a relative improvement in performance of 2.90%, 1.49% and 11.42% respectively compared with classical U-Net. In addition, we used the Tanimoto similarity to replace the Jaccard similarity for gray-to-gray image comparisons.