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Quantification of adipose tissue (fat) from computed tomography (CT) scans isconducted mostly through manual or semi-automated image segmentation algorithmswith limited efficacy. In this work, we propose a completely unsupervised andautomatic method to identify adipose tissue, and then separate SubcutaneousAdipose Tissue (SAT) from Visceral Adipose Tissue (VAT) at the abdominalregion. We offer a three-phase pipeline consisting of (1) Initial boundaryestimation using gradient points, (2) boundary refinement using GeometricMedian Absolute Deviation and Appearance based Local Outlier Scores (3) Contextdriven label fusion using Conditional Random Fields (CRF) to obtain the finalboundary between SAT and VAT. We evaluate the proposed method on 151 abdominalCT scans and obtain state-of-the-art 94% and 91% dice similarity scores for SATand VAT segmentation, as well as significant reduction in fat quantificationerror measure.

Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes