  Metabolic health is increasingly implicated as a risk factor across
conditions from cardiology to neurology, and efficiency assessment of body
composition is critical to quantitatively characterizing these relationships.
2D low dose single slice computed tomography (CT) provides a high resolution,
quantitative tissue map, albeit with a limited field of view. Although numerous
potential analyses have been proposed in quantifying image context, there has
been no comprehensive study for low-dose single slice CT longitudinal
variability with automated segmentation. We studied a total of 1816 slices from
1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset
using supervised deep learning-based segmentation and unsupervised clustering
method. 300 out of 1469 subjects that have two year gap in their first two
scans were pick out to evaluate longitudinal variability with measurements
including intraclass correlation coefficient (ICC) and coefficient of variation
(CV) in terms of tissues/organs size and mean intensity. We showed that our
segmentation methods are stable in longitudinal settings with Dice ranged from
0.821 to 0.962 for thirteen target abdominal tissues structures. We observed
high variability in most organ with ICC<0.5, low variability in the area of
muscle, abdominal wall, fat and body mask with average ICC>0.8. We found that
the variability in organ is highly related to the cross-sectional position of
the 2D slice. Our efforts pave quantitative exploration and quality control to
reduce uncertainties in longitudinal analysis.
