Influence of filter choice on 18F-FDG PET segmentation accuracy determined using generalized estimating equations

Abstract

This study aims to quantify how filter choice affects several fluoro-deoxy-glucose (FDG)-positron emission tomography (PET) segmentation methods and present the use of model fitting via generalized estimating equations (GEEs) to appropriately account for the properties of a common segmentation quality metric (Dice similarity coefficient). Spherical and irregularly shaped ‘hot’ objects filled with 18F-FDG were placed in a medium with background activity and imaged for 1, 2 and 5 min durations at low and high contrasts. Images were filtered with Gaussian and bilateral filters of 5 and 7 mm full-width half maximum (FWHM), with and without 3 mm FWHM Gaussian pre-smoothing. Four segmentation methods were used: 40% thresholding, adaptive thresholding, k-means clustering and seeded region-growing. Segmentation accuracy was quantified by overlap (using Dice similarity coefficient (DSC)) and distance between surfaces (using symmetric-mean-absolute-surface-distance (SMASD)) of the ground truth and segmented volumes. All segmentation methods showed mean DSC values between 0.71–0.87 and mean SMASD values between 0.72–2.10 mm across filters. The bilateral filter with 3 mm FWHM Gaussian pre-smoothing had mean DSC 0.80 ± 0.17 and mean SMASD 1.17 ± 1.51 mm displaying approximately equal performance to a 5 mm Gaussian filter with mean DSC 0.79 ± 0.18 and mean SMASD 1.27 ± 1.52 mm. Results from models fit using GEE with a binomial distribution and exchangeable correlation structure estimated the correlation between DSC values as 0.118 and 0.290 for spheres and irregular objects, respectively. The GEE approach accounts for several factors specific to the DSC metric that simpler statistical approaches do not, providing more accurate estimations of experimental effects commonly associated with nuclear medicine segmentation studies.