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Proceedings Paper

Unsupervised quantification of abdominal fat from CT images using Greedy Snakes
Author(s): Chirag Agarwal; Ahmed H. Dallal; Mohammad R. Arbabshirani; Aalpen Patel; Gregory Moore
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Paper Abstract

Adipose tissue has been associated with adverse consequences of obesity. Total adipose tissue (TAT) is divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Intra-abdominal fat (VAT), located inside the abdominal cavity, is a major factor for the classic obesity related pathologies. Since direct measurement of visceral and subcutaneous fat is not trivial, substitute metrics like waist circumference (WC) and body mass index (BMI) are used in clinical settings to quantify obesity. Abdominal fat can be assessed effectively using CT or MRI, but manual fat segmentation is rather subjective and time-consuming. Hence, an automatic and accurate quantification tool for abdominal fat is needed. The goal of this study is to extract TAT, VAT and SAT fat from abdominal CT in a fully automated unsupervised fashion using energy minimization techniques. We applied a four step framework consisting of 1) initial body contour estimation, 2) approximation of the body contour, 3) estimation of inner abdominal contour using Greedy Snakes algorithm, and 4) voting, to segment the subcutaneous and visceral fat. We validated our algorithm on 952 clinical abdominal CT images (from 476 patients with a very wide BMI range) collected from various radiology departments of Geisinger Health System. To our knowledge, this is the first study of its kind on such a large and diverse clinical dataset. Our algorithm obtained a 3.4% error for VAT segmentation compared to manual segmentation. These personalized and accurate measurements of fat can complement traditional population health driven obesity metrics such as BMI and WC.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332T (24 February 2017); doi: 10.1117/12.2254139
Show Author Affiliations
Chirag Agarwal, Geisinger Health System (United States)
Univ. of Illinois at Chicago (United States)
Ahmed H. Dallal, Geisinger Health System (United States)
Univ. of Pittsburgh (United States)
Mohammad R. Arbabshirani, Geisinger Health Systems (United States)
Aalpen Patel, Geisinger Health Systems (United States)
Gregory Moore, Geisinger Health Systems (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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