Deep learning–based fully automated body composition analysis of thigh CT: comparison with DXA measurement
Deep learning–based fully automated body composition analysis of thigh CT: comparison with DXA measurement
复制标题
基于深度学习的大腿 CT 全自动身体成分分析：与 DXA 测量的比较
作者：
H. Yoo;Young Jae Kim;Hyunsook Hong;S. Hong;H. Chae;J. Choi
比较容积CT与基于DL的全自动分割和双能X射线吸收测定法（DXA）在测量大腿组织成分方面的差异。该前瞻性研究于2019年1月至2020年12月进行。参与者接受DXA以确定整个身体和大腿的身体组成。在大腿区域进行CT;通过定制开发的基于DL的自动分割软件将图像自动分割为三个肌肉组和脂肪组织。随后，程序报告了大腿的组织成分。评估DXA和CT测量变量之间的相关性和一致性。然后，CT大腿组织体积预测方程的基础上DXA派生的大腿组织质量，使用一般的线性模型。共对100例患者（平均年龄44.9岁; 60例女性）进行了评价。CT值与DXA值有较强的相关性（R = 0.813~0.98，p < 0.001）。DXA和CT测量之间的总软组织肿块无显著差异（p = 0.183）。然而，DXA高估了大腿瘦（肌肉）质量，低估了大腿总脂肪质量（p < 0.001）。DXA得出的瘦体重平均比CT得出的瘦体重高10%，比CT得出的瘦肌肉质量高47%。DXA衍生的总脂肪量比CT衍生的总脂肪量低约20%。在验证组中，使用DXA衍生数据预测的CT组织体积与实际CT测量的组织体积高度相关（R2 = 0.96~0.97，p < 0.001）。基于DL的全自动分割的体积CT测量是测量大腿组织成分的快速且更准确的方法。·全身和大腿的CT和DXA测量值之间呈正相关。DXA高估了10%的大腿瘦体重，47%的瘦肌肉质量，但低估了20%的总脂肪质量相比，CT方法。·使用DXA数据（g）、年龄、身高（cm）和体重（kg）开发用于预测CT体积（cm 3）的方程，并且在验证研究中证明了良好的模型性能。
To compare volumetric CT with DL-based fully automated segmentation and dual-energy X-ray absorptiometry (DXA) in the measurement of thigh tissue composition. This prospective study was performed from January 2019 to December 2020. The participants underwent DXA to determine the body composition of the whole body and thigh. CT was performed in the thigh region; the images were automatically segmented into three muscle groups and adipose tissue by custom-developed DL-based automated segmentation software. Subsequently, the program reported the tissue composition of the thigh. The correlation and agreement between variables measured by DXA and CT were assessed. Then, CT thigh tissue volume prediction equations based on DXA-derived thigh tissue mass were developed using a general linear model. In total, 100 patients (mean age, 44.9 years; 60 women) were evaluated. There was a strong correlation between the CT and DXA measurements (R = 0.813~0.98, p < 0.001). There was no significant difference in total soft tissue mass between DXA and CT measurement (p = 0.183). However, DXA overestimated thigh lean (muscle) mass and underestimated thigh total fat mass (p < 0.001). The DXA-derived lean mass was an average of 10% higher than the CT-derived lean mass and 47% higher than the CT-derived lean muscle mass. The DXA-derived total fat mass was approximately 20% lower than the CT-derived total fat mass. The predicted CT tissue volume using DXA-derived data was highly correlated with actual CT-measured tissue volume in the validation group (R2 = 0.96~0.97, p < 0.001). Volumetric CT measurements with DL-based fully automated segmentation are a rapid and more accurate method for measuring thigh tissue composition. • There was a positive correlation between CT and DXA measurements in both the whole body and thigh. • DXA overestimated thigh lean mass by 10%, lean muscle mass by 47%, but underestimated total fat mass by 20% compared to the CT method. • The equations for predicting CT volume (cm3) were developed using DXA data (g), age, height (cm), and body weight (kg) and good model performance was proven in the validation study.
影响因子：
7
作者：
Segal, N. A.;Glass, N. A.;Torner, J.;Yang, M.;Felson, D. T.;Sharma, L.;Nevitt, M.;Lewis, C. E.
通讯作者：
Lewis, C. E.
影响因子：
3.3
作者：
Wang, W;Wang, ZM;Heymsfield, SB
通讯作者：
Heymsfield, SB
影响因子：
3.3
作者：
Visser, M;Fuerst, T;Harris, TB
通讯作者：
Harris, TB