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
为研究真实工况下人员在爆炸冲击波作用下的动态响应特性,开展某型云爆弹静爆作用下工事内仿人形装置(Anthropomorphic Test Device,ATD)和绵羊的毁伤试验研究。采用爆炸测试装置和简易假人作为研究对象,通过6发爆炸试验分析爆炸冲击波...为研究真实工况下人员在爆炸冲击波作用下的动态响应特性,开展某型云爆弹静爆作用下工事内仿人形装置(Anthropomorphic Test Device,ATD)和绵羊的毁伤试验研究。采用爆炸测试装置和简易假人作为研究对象,通过6发爆炸试验分析爆炸冲击波在ATD表面传播规律,开展2种人员损伤预测模型的对比分析。研究结果表明:在本试验工况下,冲击波和崩落的混凝土碎块是主要的毁伤元;爆炸冲击波在ATD表面首先发生反射,随后绕射至其他部位,压力曲线表现出非典型冲击波特征,反射叠加效应明显;在典型冲击波特征正压作用时间区间内,由于Axelsson损伤模型线性阻尼项的影响,求解的胸壁运动速度呈现出先增大至峰值后降低的现象;Axelsson损伤模型与UFC 3-340-02规范相比,在人员损伤预测方面相对保守。所得研究结果可为工程应用及毁伤评估提供参考。展开更多
目的:分析肩胛下动脉(SSA)系统的解剖特点,归纳其分布规律。方法:回顾性分析80例胸部增强CT图像,记录SSA的起源及长度,SSA及其分支——旋肩胛动脉(CSA)、胸背动脉(TDA)的管径及伴随静脉的归属,并进行归类。结果:160支腋动脉(AA)中,88.13...目的:分析肩胛下动脉(SSA)系统的解剖特点,归纳其分布规律。方法:回顾性分析80例胸部增强CT图像,记录SSA的起源及长度,SSA及其分支——旋肩胛动脉(CSA)、胸背动脉(TDA)的管径及伴随静脉的归属,并进行归类。结果:160支腋动脉(AA)中,88.13%(141/160)存在SSA分支,其中75.89%(107/141)起源于AA3,24.11%(34/141)起源于AA2;11.87%(19/160)SSA缺如。男性SSA起源于AA2者多于女性(25/80 vs 9/80),女性SSA缺如者多于男性(16/80 vs 3/80),性别间差异显著(P<0.01)。SSA管径为(4.72±0.76)mm,长度为(29.56±11.9)mm,CSA管径为(3.45±0.69)mm,TDA管径为(2.92±0.56)mm。SSA长度与管径性别间差异较明显(P<0.05)。SSA来源不同,分支不同(P<0.001)。AA2来源的分出胸外侧动脉(LTA)的概率高于AA3,AA3来源的发出旋肱后动脉(PCHA)的概率则高于AA2。SSA来源不同,伴随静脉汇入腋静脉点较恒定,92.52%(99/107)AA3来源、94.12%(32/34)AA2来源,52.63%(10/19)SSA缺如的分支伴随静脉汇合后,均汇入腋静脉偏外侧。结论:SSA系统变异较多,性别差异明显。胸部增强CT能清晰显示SSA系统的解剖细节,能为临床术前提供准确的血管评估。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.
文摘为研究真实工况下人员在爆炸冲击波作用下的动态响应特性,开展某型云爆弹静爆作用下工事内仿人形装置(Anthropomorphic Test Device,ATD)和绵羊的毁伤试验研究。采用爆炸测试装置和简易假人作为研究对象,通过6发爆炸试验分析爆炸冲击波在ATD表面传播规律,开展2种人员损伤预测模型的对比分析。研究结果表明:在本试验工况下,冲击波和崩落的混凝土碎块是主要的毁伤元;爆炸冲击波在ATD表面首先发生反射,随后绕射至其他部位,压力曲线表现出非典型冲击波特征,反射叠加效应明显;在典型冲击波特征正压作用时间区间内,由于Axelsson损伤模型线性阻尼项的影响,求解的胸壁运动速度呈现出先增大至峰值后降低的现象;Axelsson损伤模型与UFC 3-340-02规范相比,在人员损伤预测方面相对保守。所得研究结果可为工程应用及毁伤评估提供参考。
文摘目的:分析肩胛下动脉(SSA)系统的解剖特点,归纳其分布规律。方法:回顾性分析80例胸部增强CT图像,记录SSA的起源及长度,SSA及其分支——旋肩胛动脉(CSA)、胸背动脉(TDA)的管径及伴随静脉的归属,并进行归类。结果:160支腋动脉(AA)中,88.13%(141/160)存在SSA分支,其中75.89%(107/141)起源于AA3,24.11%(34/141)起源于AA2;11.87%(19/160)SSA缺如。男性SSA起源于AA2者多于女性(25/80 vs 9/80),女性SSA缺如者多于男性(16/80 vs 3/80),性别间差异显著(P<0.01)。SSA管径为(4.72±0.76)mm,长度为(29.56±11.9)mm,CSA管径为(3.45±0.69)mm,TDA管径为(2.92±0.56)mm。SSA长度与管径性别间差异较明显(P<0.05)。SSA来源不同,分支不同(P<0.001)。AA2来源的分出胸外侧动脉(LTA)的概率高于AA3,AA3来源的发出旋肱后动脉(PCHA)的概率则高于AA2。SSA来源不同,伴随静脉汇入腋静脉点较恒定,92.52%(99/107)AA3来源、94.12%(32/34)AA2来源,52.63%(10/19)SSA缺如的分支伴随静脉汇合后,均汇入腋静脉偏外侧。结论:SSA系统变异较多,性别差异明显。胸部增强CT能清晰显示SSA系统的解剖细节,能为临床术前提供准确的血管评估。
