Share Email Print
cover

Proceedings Paper

Fat segmentation on chest CT images via fuzzy models
Author(s): Yubing Tong; Jayaram K. Udupa; Caiyun Wu; Gargi Pednekar; Janani Rajan Subramanian; David J. Lederer; Jason Christie; Drew A. Torigian
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.

Paper Details

Date Published: 18 March 2016
PDF: 6 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978609 (18 March 2016); doi: 10.1117/12.2217864
Show Author Affiliations
Yubing Tong, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Caiyun Wu, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Gargi Pednekar, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Janani Rajan Subramanian, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
David J. Lederer, Columbia Univ. Medical Ctr. (United States)
Jason Christie, Univ. of Pennsylvania School of Medicine (United States)
Drew A. Torigian, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

© SPIE. Terms of Use
Back to Top