Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

Abstract

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.

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• A General Survey on Attention Mechanisms in Deep Learning
• Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
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• UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
• Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
• TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
• Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19
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• Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
• COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation
• D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
• Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans
• Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
• DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem
• Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging
• B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representation
• Global Convolutional Neural Processes