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
Liposarcoma is a malignant neoplasm of mesodermal origin, which among sarcomas, 10% to 20% are located in the retroperitoneum. The case presented shows a 50-year-old male patient who initially presented weight loss a...Liposarcoma is a malignant neoplasm of mesodermal origin, which among sarcomas, 10% to 20% are located in the retroperitoneum. The case presented shows a 50-year-old male patient who initially presented weight loss and abdominal pain in the left iliac fossa. A tumor mass of hardened consistency was palpated in virtually all the abdomen. An abdominal ultrasound and a computed tomography of the abdomen were performed and confirmed the tumor mass. An exploratory laparotomy was performed, with removal of bulky abdominal mass of greasy consistency. A histopathological study of the piece reported myxoid liposarcoma. Clinical and prognostic features, as well as oncologic outcomes, are well known in this group of patients. The patient has been in the outpatient clinic for 7 years without tumor recurrence. Computed tomography is the fundamental study for the diagnosis of imaging. The treatment of choice consists in an aggressive approach aiming the complete resection, which is a major predictor of local and distant recurrence and survival.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Liposarcoma is a malignant neoplasm of mesodermal origin, which among sarcomas, 10% to 20% are located in the retroperitoneum. The case presented shows a 50-year-old male patient who initially presented weight loss and abdominal pain in the left iliac fossa. A tumor mass of hardened consistency was palpated in virtually all the abdomen. An abdominal ultrasound and a computed tomography of the abdomen were performed and confirmed the tumor mass. An exploratory laparotomy was performed, with removal of bulky abdominal mass of greasy consistency. A histopathological study of the piece reported myxoid liposarcoma. Clinical and prognostic features, as well as oncologic outcomes, are well known in this group of patients. The patient has been in the outpatient clinic for 7 years without tumor recurrence. Computed tomography is the fundamental study for the diagnosis of imaging. The treatment of choice consists in an aggressive approach aiming the complete resection, which is a major predictor of local and distant recurrence and survival.
