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
A solution-treated AZ91 bulk material was deep-surface-rolled at room temperature to investigate the effect of deep surface rolling on the microstructure and mechanical properties of the alloy. Microhardness and micro...A solution-treated AZ91 bulk material was deep-surface-rolled at room temperature to investigate the effect of deep surface rolling on the microstructure and mechanical properties of the alloy. Microhardness and microstructure along the depth of the treated surface layer were characterized. The results show that the affected layer was up to 2.0 mm thick and consisted of three sublayers: a severe deformation layer with thickness of about 400 μm from the topmost surface, a medium deformation layer with thickness of around 600 μm and a small deformation layer up to 1000 μm thick. In addition to grain refinement in the deformation layer, strain-induced precipitation of β phase (Mg17Al12) was observed, particularly in the severe and medium deformation layers. It is believed that the cooperative effects of grain refinement, strain hardening and precipitation strengthening led to the significant increase in hardness of the AZ91 alloy after the deep surface rolling.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.
基金Project(2016ZE53046)supported by the Aviation Science Foundation of ChinaProject(201606295009)supported by the China Scholarship CouncilProject supported by Top International University Visiting Program for Outstanding Young Scholars of Northwestern Polytechnical University,China
文摘A solution-treated AZ91 bulk material was deep-surface-rolled at room temperature to investigate the effect of deep surface rolling on the microstructure and mechanical properties of the alloy. Microhardness and microstructure along the depth of the treated surface layer were characterized. The results show that the affected layer was up to 2.0 mm thick and consisted of three sublayers: a severe deformation layer with thickness of about 400 μm from the topmost surface, a medium deformation layer with thickness of around 600 μm and a small deformation layer up to 1000 μm thick. In addition to grain refinement in the deformation layer, strain-induced precipitation of β phase (Mg17Al12) was observed, particularly in the severe and medium deformation layers. It is believed that the cooperative effects of grain refinement, strain hardening and precipitation strengthening led to the significant increase in hardness of the AZ91 alloy after the deep surface rolling.
