Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment
Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment
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DOI：
10.1259/bjr.20190327
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发表时间：
2019-01-01
影响因子：
2.6
通讯作者：
Summers, Ronald M.
中科院分区：
文献类型：
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作者：
Graffy, Peter M.;Liu, Jiamin;Summers, Ronald M.
目的：调查一个全自动腹部CT为基础的肌肉工具在一个大的成人screeningpopulation.Methods：一个全自动验证的肌肉分割算法应用于9310非对比CT扫描，包括一个主要的筛选队列的8037个连续的无症状的成年人（平均年龄，57.1 +/- 7.8岁; 3555 M/4482 F）。在1171个个体的子集中进行了连续的随访扫描（平均间隔，5.1年），肌肉组织横截面积和衰减结果：男性两种肌肉面积的平均值均显著高于男性（190.6 ± 33.6 vs 133.3 ± 24.1 cm（2），p
Objective: To investigate a fully automated abdominal CT-based muscle tool in a large adult screening population.Methods: A fully automated validated muscle segmentation algorithm was applied to 9310 non-contrast CT scans, including a primary screening cohort of 8037 consecutive asymptomatic adults (mean age, 57.1 +/- 7.8 years; 3555M/4482F). Sequential follow-up scans were available in a subset of 1171 individuals (mean interval, 5.1 years), Muscle tissue cross-sectional area and attenuation (Hounsfield unit, HU) at the L3 level were assessed, including change over time.Results: Mean values were significantly higher in males for both muscle area (190.6 +/- 33.6 vs 133.3 +/- 24.1 cm(2), p