# Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

arXiv: https://arxiv.org/abs/1802.06955 · HTML: https://mlsanity.com/paper/1802.06955/

## Key facts

- Cited by 180 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 30 of 180)

- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2106.06716/) (2021)
- [Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks](https://mlsanity.com/paper/2106.04537/) (2021)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2107.05188/) (2021)
- [DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2202.00972/) (2022)
- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model](https://mlsanity.com/paper/2207.13415/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation](https://mlsanity.com/paper/2109.14813/) (2021)
- [SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3](https://mlsanity.com/paper/2509.00833/) (2025)
- [Dual Cross-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2303.17696/) (2023)
- [DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation](https://mlsanity.com/paper/2308.02959/) (2023)
- [An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset](https://mlsanity.com/paper/2309.09483/) (2023)
- [R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation](https://mlsanity.com/paper/2206.01793/) (2022)
- [BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation](https://mlsanity.com/paper/2402.08793/) (2024)
- [Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin](https://mlsanity.com/paper/2301.00363/) (2023)
- [The Uncanny Similarity of Recurrence and Depth](https://mlsanity.com/paper/2102.11011/) (2021)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation](https://mlsanity.com/paper/2303.01582/) (2023)
- [Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs](https://mlsanity.com/paper/2303.13211/) (2023)
- [Learning spatio-temporal patterns with Neural Cellular Automata](https://mlsanity.com/paper/2310.14809/) (2023)
- [HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN](https://mlsanity.com/paper/2110.01660/) (2021)
- [Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation](https://mlsanity.com/paper/2210.09549/) (2022)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation](https://mlsanity.com/paper/2403.09136/) (2024)
- [DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation](https://mlsanity.com/paper/2101.11183/) (2021)

## Abstract (arXiv)

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).