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Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics.

ABSTRACT: In this work the authors compare the accuracy of two-dimensional (2D) and three-dimensional (3D) implementations of a computer-aided image segmentation method to that of physician observers (using manual outlining) for volume measurements of liver tumors visualized with diagnostic contrast-enhanced and PET/CT-based non-contrast-enhanced (PET-CT) CT scans. The method assessed is a hybridization of the watershed method using observer-set markers with a gradient vector flow approach. This method is known as the iterative watershed segmentation (IWS) method. Initial assessments are performed using software phantoms that model a range of tumor shapes, noise levels, and noise qualities. IWS is then applied to CT image sets of patients with identified hepatic tumors and compared to the physicians' manual outlines on the same tumors. The repeatability of the physicians' measurements is also assessed. IWS utilizes multiple levels of segmentation performed with the use of "fuzzy regions" that could be considered part of a selected tumor. In phantom studies, the outermost volume outline for level 1 (called level 1_1 consisting of inner region plus fuzzy region) was generally the most accurate. For in vivo studies, the level 1_1 and the second outermost outline for level 2 (called level 2_2 consisting of inner region plus two fuzzy regions) typically had the smallest percent error values when compared to physician observer volume estimates. Our data indicate that allowing the operator to choose the "best result" level iteration outline from all generated outlines would likely give the more accurate volume for a given tumor rather than automatically choosing a particular level iteration outline. The preliminary in vivo results indicate that 2D-IWS is likely to be more accurate than 3D-IWS in relation to the observer volume estimates.

SUBMITTER: Ray S

PROVIDER: S-EPMC2673614 | biostudies-other | 2008 Dec

REPOSITORIES: biostudies-other

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Project description:Histological analysis is considered to be the gold standard method of evaluating osseointegration around a bone-implant. However, this method requires invasive specimen preparation and is capable of representing only one plane. By comparison, micro-computed tomography (μCT) is a fast and convenient method that offers three-dimensional information but is hampered by problems related to resolution and artifacts, making it a supplementary method for osseointegration analysis. To verify the reliability of μCT for osseointegration evaluation, this animal model study compared bone-to-implant contact (BIC) ratios obtained by the gold standard histomorphometric method with those obtained by the μCT method, using a rabbit tibia implant model. A sandblasted, large-grit, acid-etched (SLA) implant and a machined surface implant were inserted into each tibia of two rabbits (giving eight implants in total). Bone-implant specimens were analyzed using μCT with a spiral scan technique (SkyScan 1275) and histological sections were prepared thereafter. Three-dimensional (3D) reconstructed μCT data and four two-dimensional (2D) μCT sections, including one section corresponding to the histologic section and three additional sections rotated 45°, 90°, and 135°, were used to calculate the BIC ratio. The Pearson's test was used for correlation analysis at a significance level of 0.05. The histomorphometric BIC and the 2D-μCT BIC showed strong correlation (r = 0.762, P = 0.046), whereas the histomorphometric BIC and 3D-μCT BIC did not (r = -0.375, P = 0.385). However, the mean BIC value of three or four 2D-μCT sections showed a strong correlation with the 3D-μCT BIC (three sections: r = 0.781, P = 0.038; four sections: r = 0.804, P = 0.029). The results of this animal model study indicate that μCT can be used to complement the histomorphometric method in bone-implant interface analyses. With the limitations of this study, 3D-μCT analysis may even have a superior aspect in that it eliminates random variables that arise as a consequence of the selected cutting direction.

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Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics.

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