| 杨洁,刘艳丽,陈小燕,陈天乐,刘奇.基于胸部CT身体成分自动分析系统评估肌肉及脂肪含量[J].中国医学影像技术,2024,40(8):1242~1248 |
| 基于胸部CT身体成分自动分析系统评估肌肉及脂肪含量 |
| Automated body composition analysis system based on chest CT for evaluating content of muscle and adipose |
| 投稿时间：2024-03-13 修订日期：2024-05-13 |
| DOI：10.13929/j.issn.1003-3289.2024.08.028 |
| 中文关键词: 身体成分 胸部 肌,骨骼 脂肪组织 深度学习 体层摄影术,X线计算机 |
| 英文关键词:body composition thorax muscle, skeletal adipose tissue deep learning tomography, X-ray computed |
| 基金项目:自贡市市级科技计划项目(2021YXY12)。 |
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| 中文摘要: |
| 目的 构建基于胸部CT的身体成分自动分析系统,观察其评估胸部肌肉及脂肪含量的价值。方法 收集108例肺炎患者T7~T8层面轴位胸部CT图像(分割数据集),于COVID 19-CT数据集筛选984例胸部CT数据(随机抽取10例为整体测试数据集,余974例为选层数据集);基于卷积神经网络(CNN)衍生网络,包括ResNet、ResNeXt、MobileNet、ShuffleNet、DenseNet、EfficientNet及ConvNeXt,于选层数据集中分类T7~T8层面,以准确率、精确率、召回率及特异度进行评价;基于经典全CNN(FCN)衍生网络,包括FCN、SegNet、UNet、Attention UNet、UNet++、nnUNet、UNeXt及CMUNeXt于分割数据集中分割骨骼肌(SM)、皮下脂肪组织(SAT)、肌间脂肪组织(IMAT)及内脏脂肪组织(VAT),以戴斯相似系数(DSC)、交并比(IoU)及95豪斯多夫距离(HD)进行评价;基于表现最优的选层网络及分层网络构建身体成分自动分析系统,对整体测试数据集进行测试,以平均绝对误差(MAE)、均方根误差(RMSE)及MAE的标准差(SD)进行评价。结果 DenseNet网络自动于完整胸部CT图中分类T7~T8层面的准确率、精确率、召回率及特异度分别为95.06%、84.83%、92.27%及95.78%,均高于其余选层网络。在分割SM、SAT、IMAT及整体分割方面,UNet++网络DSC及IoU均高于、而95HD均低于其余分割网络。以DenseNet为选层网络、UNet++为分割网络测试整体测试数据集,其预测SM、SAT、IMAT及VAT的MAE分别为27.09、6.95、6.65及3.35 cm2。结论 基于胸部CT身体成分自动分析系统可用于评估胸部肌肉及脂肪含量;其中最佳分割网络UNet++分割脂肪组织精准度优于SM。 |
| 英文摘要: |
| 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 cm2, 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. |
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