Modality specific U-Net variants for biomedical image segmentation: A survey

Abstract

With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing 1) inter-modality, and 2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.

References in corpus (47)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Survey on Deep Learning in Medical Image Analysis
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• Rethinking Atrous Convolution for Semantic Image Segmentation
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Attention U-Net: Learning Where to Look for the Pancreas
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Fully Convolutional Networks for Semantic Segmentation
• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
• CE-Net: Context Encoder Network for 2D Medical Image Segmentation
• Squeeze-and-Excitation Networks
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• Medical Image Fusion: A survey of the state of the art
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
• Deep learning for cardiac image segmentation: A review
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• A review: Deep learning for medical image segmentation using multi-modality fusion
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• Recent progress in semantic image segmentation
• Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
• Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
• Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
• Face Mask Detection using Transfer Learning of InceptionV3
• Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
• Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
• Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation
• Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
• COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
• DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
• RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging
• COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
• Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
• Deep learning trends for focal brain pathology segmentation in MRI
• Unleashing the power of disruptive and emerging technologies amid COVID-19: A detailed review
• A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images
• Direct Uncertainty Prediction for Medical Second Opinions
• CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
• Segmentation Loss Odyssey
• Mixed Transformer U-Net For Medical Image Segmentation
• MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
• Artificial Intelligence in Quantitative Ultrasound Imaging: A Review

Cited by in corpus (5)

• CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
• Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture
• MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification
• Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction
• IODeep: an IOD for the introduction of deep learning in the DICOM standard