The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is
fraught with failure; recurrence rates ranging from 24-43% have been reported, even with the use of biocompatible
mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical
judgments; notably, quantitative metrics based on image-processing are not used. We propose that image segmentation
methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation
on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. To
date, automated segmentation algorithms have not been presented to quantify the abdominal wall and potential hernias.
In this pilot study with four clinically acquired CT scans on post-operative patients, we demonstrate a novel approach to
geometric classification of the abdominal wall and essential abdominal features (including bony landmarks and skin
surfaces). Our approach uses a hierarchical design in which the abdominal wall is isolated in the context of the skin and
bony structures using level set methods. All segmentation results were quantitatively validated with surface errors based
on manually labeled ground truth. Mean surface errors for the outer surface of the abdominal wall was less than 2mm.
This approach establishes a baseline for characterizing the abdominal wall for improving VH care.