# R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2206.01793 · HTML: https://mlsanity.com/paper/2206.01793/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [CNN-based Segmentation of Medical Imaging Data](https://mlsanity.com/paper/1701.03056/) (2017)
- [MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation](https://mlsanity.com/paper/1812.00352/) (2018)
- [Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images](https://mlsanity.com/paper/2004.05645/) (2020)
- [COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution](https://mlsanity.com/paper/2004.10987/) (2020)
- [DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation](https://mlsanity.com/paper/1709.00201/) (2017)
- [ErrorNet: Learning error representations from limited data to improve vascular segmentation](https://mlsanity.com/paper/1910.04814/) (2019)

## Built on by (most cited first, 11 of 11)

- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices](https://mlsanity.com/paper/2206.02358/) (2022)
- [A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers](https://mlsanity.com/paper/2407.05163/) (2024)
- [CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals](https://mlsanity.com/paper/2502.17536/) (2025)
- [Learned Image Compression and Restoration for Digital Pathology](https://mlsanity.com/paper/2503.23862/) (2025)
- [MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation](https://mlsanity.com/paper/2305.03617/) (2023)
- [Active Inference-Based Optimization of Discriminative Neural Network Classifiers](https://mlsanity.com/paper/2306.02447/) (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)
- [Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images](https://mlsanity.com/paper/2310.05697/) (2023)
- [Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation](https://mlsanity.com/paper/2405.13304/) (2024)
- [Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images](https://mlsanity.com/paper/2509.25549/) (2025)

## Abstract (arXiv)

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy (EM), X-rays, fundus, and computed tomography (CT). The average gain achieved in IoU score is 1.5+-0.37% and in dice score is 0.9+-0.33% over UNET++, whereas, 4.21+-2.72 in IoU and 3.47+-1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.