COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can rang...COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.展开更多
The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and...The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.展开更多
Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LU...Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LUS) in resource-rich ICUs is still under investigation. The present study compares the utility of LUS to that of CXR in identifying pulmonary edema and pleural effusion in ICU patients. In addition, consolidation and pneumothorax were analyzed as secondary outcome measures. Material and Methods This is a prospective, single centric, observational study. Patients admitted in ICU were examined for lung pathologies, using LUS by a trained intensivist;and CXR done within 4 hours of each other. The final diagnosis was ascertained by an independent senior radiologist, based on the complete medical chart including clinical findings and the results of thoracic CT, if available. The results were compared and analyzed. Results Sensitivity, specificity and diagnostic accuracy of LUS was 95%, 94.4%, 94.67% for pleural effusion;and 98.33%, 97.78%, 98.00% for pulmonary edema respectively. Corresponding values with CXR were 48.33%, 76.67%, 65.33% for pleural effusion;and 36.67%, 82.22% and 64.00% for pulmonary edema respectively. Sensitivity, specificity and diagnostic accuracy of LUS was 91.30%, 96.85%, 96.00% for consolidation;and 100.00%, 79.02%, 80.00% for pneumothorax respectively. Corresponding values with CXR were 60.87%, 81.10%, 78.00% for consolidation;and 71.3%, 97.20%, 96.00% for pneumothorax respectively. Conclusion LUS has better diagnostic accuracy in diagnosis of pleural effusion and pulmonary edema when compared with CXR and is thus recommended as an effective alternative for diagnosis of these conditions in acute care settings. Our study recommends that a thoracic CT scan can be avoided in most of such cases.展开更多
<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) syst...<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.</span>展开更多
A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defec...A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.展开更多
Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不...胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不平衡、卷积神经网络参数量过大等问题.针对上述问题,提出了一种端到端的基于八度卷积的ResNet(octave convolution based residual network,OC-ResNet)结构.首先,利用八度卷积改进ResNet中的普通卷积,将高低频特征分离,增强对高频信息的提取,以更好地表达胸部病灶的特异性特征,降低模型计算复杂度.其次,利用渐进式迁移学习,将OC-ResNet在ImageNet数据集进行预训练,获得网络的初始参数,然后固定网络浅层参数,在ChestX-Ray14数据集上微调网络深层参数.最后,为改善样本不平衡问题,网络训练时,采用了焦点损失函数,增加样本数较少类别的权重.在ChestX-Ray14数据集上的实验结果表明,OC-ResNet对14种胸部疾病分类的平均AUC值达到0.856,与目前先进的深度学习方法相比,其中13种疾病分类的AUC值达到最优,同时,计算复杂度相比基础网络降低了44.77%.展开更多
目的:为了在数字化摄影中选择合适的射线能量,探讨不同管电压下胸部数字X线摄影影像效果与其对患者的影响。方法:选择成人胸部模体为研究对象,将管电压分别设为80、100及120 k V进行数字X线曝光,测量每种管电压下模体入射剂量、相对噪...目的:为了在数字化摄影中选择合适的射线能量,探讨不同管电压下胸部数字X线摄影影像效果与其对患者的影响。方法:选择成人胸部模体为研究对象,将管电压分别设为80、100及120 k V进行数字X线曝光,测量每种管电压下模体入射剂量、相对噪声值、对比度噪声比及有效剂量。结果:自动曝光控制挡位保持不动时,有效剂量及模体入射剂量均随着管电压的增加而逐渐减小,且模体入射剂量与有效剂量呈正相关性;相对噪声值会随着管电压的增加而逐渐减小,对比度噪声比会随着管电压的增加而逐渐增大。结论:进行胸部数字X线摄影时,在不影响影像质量的前提下,为了降低受检患者所受的辐射剂量应尽量使用较高的管电压。展开更多
文摘COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-096)the National Major Scientific Instruments and Equipment Development Project of China(No.11627901)+1 种基金the National Key Research and Development Program of China(Nos.2021YFF0701202,2021YFA1600703)the National Natural Science Foundation of China(Nos.U1932205,12275343).
文摘The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.
文摘Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LUS) in resource-rich ICUs is still under investigation. The present study compares the utility of LUS to that of CXR in identifying pulmonary edema and pleural effusion in ICU patients. In addition, consolidation and pneumothorax were analyzed as secondary outcome measures. Material and Methods This is a prospective, single centric, observational study. Patients admitted in ICU were examined for lung pathologies, using LUS by a trained intensivist;and CXR done within 4 hours of each other. The final diagnosis was ascertained by an independent senior radiologist, based on the complete medical chart including clinical findings and the results of thoracic CT, if available. The results were compared and analyzed. Results Sensitivity, specificity and diagnostic accuracy of LUS was 95%, 94.4%, 94.67% for pleural effusion;and 98.33%, 97.78%, 98.00% for pulmonary edema respectively. Corresponding values with CXR were 48.33%, 76.67%, 65.33% for pleural effusion;and 36.67%, 82.22% and 64.00% for pulmonary edema respectively. Sensitivity, specificity and diagnostic accuracy of LUS was 91.30%, 96.85%, 96.00% for consolidation;and 100.00%, 79.02%, 80.00% for pneumothorax respectively. Corresponding values with CXR were 60.87%, 81.10%, 78.00% for consolidation;and 71.3%, 97.20%, 96.00% for pneumothorax respectively. Conclusion LUS has better diagnostic accuracy in diagnosis of pleural effusion and pulmonary edema when compared with CXR and is thus recommended as an effective alternative for diagnosis of these conditions in acute care settings. Our study recommends that a thoracic CT scan can be avoided in most of such cases.
文摘<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.</span>
文摘A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
文摘胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不平衡、卷积神经网络参数量过大等问题.针对上述问题,提出了一种端到端的基于八度卷积的ResNet(octave convolution based residual network,OC-ResNet)结构.首先,利用八度卷积改进ResNet中的普通卷积,将高低频特征分离,增强对高频信息的提取,以更好地表达胸部病灶的特异性特征,降低模型计算复杂度.其次,利用渐进式迁移学习,将OC-ResNet在ImageNet数据集进行预训练,获得网络的初始参数,然后固定网络浅层参数,在ChestX-Ray14数据集上微调网络深层参数.最后,为改善样本不平衡问题,网络训练时,采用了焦点损失函数,增加样本数较少类别的权重.在ChestX-Ray14数据集上的实验结果表明,OC-ResNet对14种胸部疾病分类的平均AUC值达到0.856,与目前先进的深度学习方法相比,其中13种疾病分类的AUC值达到最优,同时,计算复杂度相比基础网络降低了44.77%.
文摘目的:为了在数字化摄影中选择合适的射线能量,探讨不同管电压下胸部数字X线摄影影像效果与其对患者的影响。方法:选择成人胸部模体为研究对象,将管电压分别设为80、100及120 k V进行数字X线曝光,测量每种管电压下模体入射剂量、相对噪声值、对比度噪声比及有效剂量。结果:自动曝光控制挡位保持不动时,有效剂量及模体入射剂量均随着管电压的增加而逐渐减小,且模体入射剂量与有效剂量呈正相关性;相对噪声值会随着管电压的增加而逐渐减小,对比度噪声比会随着管电压的增加而逐渐增大。结论:进行胸部数字X线摄影时,在不影响影像质量的前提下,为了降低受检患者所受的辐射剂量应尽量使用较高的管电压。
