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

Abstract

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).

12 pages, 21 figures, 3 Tables

References in corpus (13)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Survey on Deep Learning in Medical Image Analysis
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• Brain Tumor Segmentation with Deep Neural Networks
• Road Extraction by Deep Residual U-Net
• Conditional Random Fields as Recurrent Neural Networks
• Densely Connected Convolutional Networks
• Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
• The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
• On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
• CNN-based Segmentation of Medical Imaging Data
• VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
• Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network

Cited by in corpus (64)

• U-Net and its variants for medical image segmentation: theory and applications
• Recent advances and clinical applications of deep learning in medical image analysis
• DUNet: A deformable network for retinal vessel segmentation
• RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
• Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
• A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
• Modality specific U-Net variants for biomedical image segmentation: A survey
• LadderNet: Multi-path networks based on U-Net for medical image segmentation
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
• Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
• Optimized Deep Encoder-Decoder Methods for Crack Segmentation
• From Dark Matter to Galaxies with Convolutional Networks
• CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
• U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
• Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network
• Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
• Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
• Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation
• DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation
• ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
• From Dark Matter to Galaxies with Convolutional Neural Networks
• Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
• A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
• Hierarchical Attention Networks for Medical Image Segmentation
• Deep learning in magnetic resonance prostate segmentation: A review and a new perspective
• Transfer Learning with Edge Attention for Prostate MRI Segmentation
• A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset
• Image segmentation via Cellular Automata
• Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
• Efficient Palm-Line Segmentation with U-Net Context Fusion Module
• BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation
• Retinal Vessel Segmentation based on Fully Convolutional Networks
• Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation
• Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images
• Residual Spatial Attention Network for Retinal Vessel Segmentation
• Object Based Attention Through Internal Gating
• Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
• Channel Attention Residual U-Net for Retinal Vessel Segmentation
• MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
• MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
• Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound
• Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation
• Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images
• Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks
• Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection
• Automatic Data Augmentation for 3D Medical Image Segmentation
• Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
• Feedback U-net for Cell Image Segmentation
• Refined Deep Neural Network and U-Net for Polyps Segmentation
• HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN
• The Uncanny Similarity of Recurrence and Depth
• Improving Lesion Detection by exploring bias on Skin Lesion dataset
• SVS-net: A Novel Semantic Segmentation Network in Optical Coherence Tomography Angiography Images
• Coronary Wall Segmentation in CCTA Scans via a Hybrid Net with Contours Regularization
• Feedback Attention for Cell Image Segmentation
• Fastidious Attention Network for Navel Orange Segmentation
• Unsupervised learning of the brain connectivity dynamic using residual D-net
• R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
• Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging
• Hoechst Is All You Need: Lymphocyte Classification with Deep Learning
• A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
• Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning
• Optimization of Image Embeddings for Few Shot Learning
• Generative Model for Material Experiments Based on Prior Knowledge and Attention Mechanism