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
针对浮式消波海流机样机测试时出现的问题,对其吃水深度线和叶片安装角度进行优化。根据装置结构特点,用Gambit建立了装置横截面的二维网格模型。根据浮式消波海流机实际工作环境,利用Fluent流体仿真软件,使用VOF(volume of fluid)两相...针对浮式消波海流机样机测试时出现的问题,对其吃水深度线和叶片安装角度进行优化。根据装置结构特点,用Gambit建立了装置横截面的二维网格模型。根据浮式消波海流机实际工作环境,利用Fluent流体仿真软件,使用VOF(volume of fluid)两相流模型,分配空气相与液态水相在流域中的不同比例,来确定不同的吃水深度线。并且结合k-epsilon紊流模型建立模拟仿真环境。先对3种水线进行仿真分析,然后进行实验验证。通过分析对比装置的3种不同吃水深度线的模拟与实验结果,得到装置的最优吃水深度线为1/3水线。基于最优吃水深度线,分别对叶片的4种安装角度在相同的仿真环境中进行模拟仿真。利用仿真得到的扭矩数据,计算扭矩系数、功率系数,分析对比仿真数据以及仿真过程中扭矩趋势图得出4种叶片安装角度的最优角度是90°。吃水深度线决定了装置的消波能力,并且为叶片获能提供条件,叶片安装角度直接影响着装置的获能效率,所以最优的吃水深度线和叶片安装角度对海流机的消波能力和获能效率具有重要意义。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.
文摘针对浮式消波海流机样机测试时出现的问题,对其吃水深度线和叶片安装角度进行优化。根据装置结构特点,用Gambit建立了装置横截面的二维网格模型。根据浮式消波海流机实际工作环境,利用Fluent流体仿真软件,使用VOF(volume of fluid)两相流模型,分配空气相与液态水相在流域中的不同比例,来确定不同的吃水深度线。并且结合k-epsilon紊流模型建立模拟仿真环境。先对3种水线进行仿真分析,然后进行实验验证。通过分析对比装置的3种不同吃水深度线的模拟与实验结果,得到装置的最优吃水深度线为1/3水线。基于最优吃水深度线,分别对叶片的4种安装角度在相同的仿真环境中进行模拟仿真。利用仿真得到的扭矩数据,计算扭矩系数、功率系数,分析对比仿真数据以及仿真过程中扭矩趋势图得出4种叶片安装角度的最优角度是90°。吃水深度线决定了装置的消波能力,并且为叶片获能提供条件,叶片安装角度直接影响着装置的获能效率,所以最优的吃水深度线和叶片安装角度对海流机的消波能力和获能效率具有重要意义。
