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Proceedings Paper

Whole abdominal wall segmentation using augmented active shape models (AASM) with multi-atlas label fusion and level set
Author(s): Zhoubing Xu; Rebeccah B. Baucom; Richard G. Abramson; Benjamin K. Poulose; Bennett A. Landman
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Paper Abstract

The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole abdominal wall on routinely acquired computed tomography (CT) scans remains challenging due to variations and complexities of the wall and surrounding tissues. In this study, we propose a slice-wise augmented active shape model (AASM) approach to robustly segment both the outer and inner surfaces of the abdominal wall. Multi-atlas label fusion (MALF) and level set (LS) techniques are integrated into the traditional ASM framework. The AASM approach globally optimizes the landmark updates in the presence of complicated underlying local anatomical contexts. The proposed approach was validated on 184 axial slices of 20 CT scans. The Hausdorff distance against the manual segmentation was significantly reduced using proposed approach compared to that using ASM, MALF, and LS individually. Our segmentation of the whole abdominal wall enables the subcutaneous and visceral fat measurement, with high correlation to the measurement derived from manual segmentation. This study presents the first generic algorithm that combines ASM, MALF, and LS, and demonstrates practical application for automatically capturing visceral and subcutaneous fat volumes.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840U (21 March 2016); doi: 10.1117/12.2216841
Show Author Affiliations
Zhoubing Xu, Vanderbilt Univ. (United States)
Rebeccah B. Baucom, Vanderbilt Univ. Medical Ctr. (United States)
Richard G. Abramson, Vanderbilt Univ. (United States)
Benjamin K. Poulose, Vanderbilt Univ. Medical Ctr. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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