Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common side effects of surgical intervention. Repair of VHs is the most commonly performed procedure by general surgeons worldwide, but VH repair outcomes are not particularly encouraging (with recurrence rates up to 43%). A variety of open and laparoscopic techniques are available for hernia repair, and the specific technique used is ultimately driven by surgeon preference and experience. Despite routine acquisition of computed tomography (CT) for VH patients, little quantitative information is available on which to guide selection of a particular approach and/or optimize patient-specific treatment. From anecdotal interviews, the success of VH repair procedures correlates with hernia size, location, and involvement of secondary structures. Herein, we propose an image labeling protocol to segment the anterior abdominal area to provide a geometric basis with which to derive biomarkers and evaluate treatment efficacy. Based on routine clinical CT data, we are able to identify inner and outer surfaces of the abdominal walls and the herniated volume. This is the first formal presentation of a protocol to quantify these structures on abdominal CT. The intra- and inter rater reproducibilities of this protocol are evaluated on 4 patients with suspected VH (3 patients were ultimately diagnosed with VH while 1 was not). Mean surfaces distances of less than 2mm were achieved for all structures.
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.