Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
目的:为了实现对人体生理健康状况的无线监测,设计一种集成体温和皮肤水化传感的近场通信(near field communication,NFC)系统。方法:该系统由NFC标签端(体温和皮肤水化监测终端)、NFC协议读写器端(NFC读写器或支持NFC协议的手机)和上位...目的:为了实现对人体生理健康状况的无线监测,设计一种集成体温和皮肤水化传感的近场通信(near field communication,NFC)系统。方法:该系统由NFC标签端(体温和皮肤水化监测终端)、NFC协议读写器端(NFC读写器或支持NFC协议的手机)和上位机(PC或支持NFC协议的手机)3个部分组成。NFC标签端完成体温和皮肤水化生理参数的采集,包含NFC标签芯片(选用RF430FRL152H芯片)、体温传感电路和皮肤水化传感电路;NFC协议读写器端以无线方式读取NFC标签端数据,并将数据转发至上位机进行处理与显示,其中NFC读写器选用TRF7970A评估板;上位机采用状态机的方法进行编程,其中PC程序采用LabVIEW语言开发,手机端应用程序是由德州仪器开发的适用于Android手机的应用程序。对该系统测量体温和皮肤水化的效果进行模拟实验,并进行无线能量传输距离测量实验。结果:实验表明,NFC标签端能够测量体温和皮肤水化参数,其中温度测量精度为0.1℃,皮肤水化测量结果与皮肤水化测试仪的测量结果近似;将TRF7970A评估板作为读写器端时,测得最大传输距离为3.6 cm,使用支持NFC协议的手机作为读写器端时,测得最大传输距离为1.0 cm。结论:该系统可以实时监测体温和皮肤水化变化情况,且具有小型化、无电池和无线连接的特性,能够用于医疗保健和健康监测。展开更多
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
文摘目的:为了实现对人体生理健康状况的无线监测,设计一种集成体温和皮肤水化传感的近场通信(near field communication,NFC)系统。方法:该系统由NFC标签端(体温和皮肤水化监测终端)、NFC协议读写器端(NFC读写器或支持NFC协议的手机)和上位机(PC或支持NFC协议的手机)3个部分组成。NFC标签端完成体温和皮肤水化生理参数的采集,包含NFC标签芯片(选用RF430FRL152H芯片)、体温传感电路和皮肤水化传感电路;NFC协议读写器端以无线方式读取NFC标签端数据,并将数据转发至上位机进行处理与显示,其中NFC读写器选用TRF7970A评估板;上位机采用状态机的方法进行编程,其中PC程序采用LabVIEW语言开发,手机端应用程序是由德州仪器开发的适用于Android手机的应用程序。对该系统测量体温和皮肤水化的效果进行模拟实验,并进行无线能量传输距离测量实验。结果:实验表明,NFC标签端能够测量体温和皮肤水化参数,其中温度测量精度为0.1℃,皮肤水化测量结果与皮肤水化测试仪的测量结果近似;将TRF7970A评估板作为读写器端时,测得最大传输距离为3.6 cm,使用支持NFC协议的手机作为读写器端时,测得最大传输距离为1.0 cm。结论:该系统可以实时监测体温和皮肤水化变化情况,且具有小型化、无电池和无线连接的特性,能够用于医疗保健和健康监测。
