# MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

arXiv: https://arxiv.org/abs/1812.00352 · HTML: https://mlsanity.com/paper/1812.00352/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 27 of 27)

- [Dense Nested Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2106.00487/) (2021)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2311.17791/) (2023)
- [DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs](https://mlsanity.com/paper/2403.19588/) (2024)
- [ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation](https://mlsanity.com/paper/2102.00221/) (2021)
- [R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation](https://mlsanity.com/paper/2206.01793/) (2022)
- [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://mlsanity.com/paper/2502.06895/) (2025)
- [Pyramid U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2104.02333/) (2021)
- [RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2506.02393/) (2025)
- [SAMRI: Segment Any MRI](https://mlsanity.com/paper/2510.26635/) (2025)
- [Cell image segmentation by Feature Random Enhancement Module](https://mlsanity.com/paper/2101.07983/) (2021)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation](https://mlsanity.com/paper/2109.10083/) (2021)
- [Towards Robotic Knee Arthroscopy: Multi-Scale Network for Tissue-Tool Segmentation](https://mlsanity.com/paper/2110.02657/) (2021)
- [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)
- [ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation](https://mlsanity.com/paper/2206.05225/) (2022)
- [DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images](https://mlsanity.com/paper/2211.14235/) (2022)
- [Deep Attention Unet: A Network Model with Global Feature Perception Ability](https://mlsanity.com/paper/2304.10829/) (2023)
- [An image segmentation algorithm based on multi-scale feature pyramid network](https://mlsanity.com/paper/2305.10631/) (2023)
- [Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging](https://mlsanity.com/paper/2306.11137/) (2023)
- [LadleNet: A Two-Stage UNet for Infrared Image to Visible Image Translation Guided by Semantic Segmentation](https://mlsanity.com/paper/2308.06603/) (2023)
- [Multiclass Segmentation using Teeth Attention Modules for Dental X-ray Images](https://mlsanity.com/paper/2311.03749/) (2023)
- [IODeep: an IOD for the introduction of deep learning in the DICOM standard](https://mlsanity.com/paper/2311.16163/) (2023)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation](https://mlsanity.com/paper/2409.20414/) (2024)
- [MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation](https://mlsanity.com/paper/2506.12441/) (2025)
- [Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection](https://mlsanity.com/paper/2507.00903/) (2025)

## Abstract (arXiv)

Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions to biomedical image segmentation applications. In this paper, we propose three different multi-scale dense connections (MDC) for the encoder, the decoder of U-shaped architectures, and across them. Based on three dense connections, we propose a multi-scale densely connected U-Net (MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the neighboring feature maps with different scales from both higher layers and lower layers to strengthen feature propagation in the current layer. Multi-scale dense connections, which contain shorter connections between layers close to the input and output, also make a much deeper U-Net possible. Besides, we introduce quantization to alleviate the potential overfitting in dense connections, and further improve the segmentation performance. We evaluate our proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three MDC improve U-Net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile, the MDU-Net with quantization obviously improves the segmentation performance of original U-Net.