  Quantification of adipose tissue (fat) from computed tomography (CT) scans is
conducted mostly through manual or semi-automated image segmentation algorithms
with limited efficacy. In this work, we propose a completely unsupervised and
automatic method to identify adipose tissue, and then separate Subcutaneous
Adipose Tissue (SAT) from Visceral Adipose Tissue (VAT) at the abdominal
region. We offer a three-phase pipeline consisting of (1) Initial boundary
estimation using gradient points, (2) boundary refinement using Geometric
Median Absolute Deviation and Appearance based Local Outlier Scores (3) Context
driven label fusion using Conditional Random Fields (CRF) to obtain the final
boundary between SAT and VAT. We evaluate the proposed method on 151 abdominal
CT scans and obtain state-of-the-art 94% and 91% dice similarity scores for SAT
and VAT segmentation, as well as significant reduction in fat quantification
error measure.
