ABSTRACT
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
ABSTRACT
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power
ABSTRACT
This study aims to explore the changes of Chinese coronavirus disease-2019 (COVID-19) policy topics in the eclipse, outbreak, and convalescent stage of COVID-19 based on 4982 textual policies. By using the co-occurrence clustering network method, we find that the strict prevention and control of the epidemic is the only topic of policies in the eclipse stage. In the outbreak stage, strict epidemic prevention and control is still the most important policy topic. The policies of resuming work of “essential”enterprises and stabilizing market prices are important support and guarantee for fighting against COVID-19. In the convalescent stage, as the prevention and control of COVID-19 has become regular, promoting and ensuring the resumption of work in all sectors of society is the most important topic of the policies. Moreover, the success of Wuhan City’s fight against COVID-19 reflects China’s governance characteristics of “concentrating power to do a major event”. Finally, the possible improvements for Chinese COVID-19 policies are discussed, which can provide practical suggestions for government departments on how to effectively respond to public health emergencies.
ABSTRACT
PURPOSE: We aimed to compare chest HRCT lung signs identified in scans of differently aged patients with COVID-19 infections. METHODS: Case data of patients diagnosed with COVID-19 infection in Hangzhou City, Zhejiang Province in China were collected, and chest HRCT signs of infected patients in four age groups (<18 years, 18-44 years, 45-59 years, ≥60 years) were compared. RESULTS: Small patchy, ground-glass opacity (GGO), and consolidations were the main HRCT signs in 98 patients with confirmed COVID-19 infections. Patients aged 45-59 years and aged ≥60 years had more bilateral lung, lung lobe, and lung field involvement, and greater lesion numbers than patients <18 years. GGO accompanied with the interlobular septa thickening or a crazy-paving pattern, consolidation, and air bronchogram sign were more common in patients aged 45-59 years, and ≥60 years, than in those aged <18 years, and aged 18-44 years. CONCLUSIONS: Chest HRCT manifestations in patients with COVID-19 are related to patient's age, and HRCT signs may be milder in younger patients.