Sarcopenia is increasingly identified as a correlate of frailty and ageing and associated with an increased likelihood of falls, fracture, frailty and mortality. The gold standard for the sarcopenia evaluation in computed tomography (CT) scan was psoas muscle area (PMA) measurement. In this paper, we proposed an automated deep learning approach to find the muscle volume and assessed the correlation between PMA and muscle volume in the chest CT. This alternate muscle volume metric becomes significant since most chest CT scans taken to assess lung diseases might not consist of psoas muscle but consists of other muscles, and it would therefore not be possible to assess sarcopenia in chest CT. Our results show a good correlation between the psoas muscle area and the muscle volume produced over specific anatomical landmarks by segmenting the muscle tissue using the 2D U-Net segmentation model, strengthening our proposition. Along with the muscle volume, we have also found the volume of peripheral fat and have shown there exists a correlation between them which could be helpful for nutritional evaluation. © 2022 IEEE.