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 (18)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Fully Convolutional Networks for Semantic Segmentation
• CE-Net: Context Encoder Network for 2D Medical Image Segmentation
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
• Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
• 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
• Deep learning trends for focal brain pathology segmentation in MRI
• 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

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• IODeep: an IOD for the introduction of deep learning in the DICOM standard