A phantom study to assess the reproducibility, robustness and accuracy of PET image segmentation methods against statistical fluctuations.

Abstract

BackgroundAutomatic and semi-automatic segmentation methods for PET serve as alternatives to manual delineation and eliminate observer variability. The robustness of these segmentation methods against statistical fluctuations arising from variable size, contrast and noise are vital for providing reliable clinical outcomes for diagnosis and treatment response assessment. In this study, the performances of several segmentation methods have been investigated using the torso NEMA phantom against statistical fluctuations.MethodsThe six hot spheres (0.5-27ml) and the background of the phantom were filled with different activities of 18F to yield 2:1 and 4:1 contrast ratios. The phantom was scanned on a TrueV PET-CT scanner for 120 minutes. The images were reconstructed using OSEM (4iterations-21subsets) for different durations (15, 20, 34 and 67 minutes) to represent different noise levels and smoothed with a 4-mm Gaussian filter. Each sphere with different settings was delineated using a fixed 40% threshold (40T), fuzzy clustering mean (FCM), adaptive threshold and region based variational (C-V) segmentation methods and compared with the gold standard volume, which was estimated from the known diameter and position of each sphere.ResultsThe smallest three spheres at the 2:1 contrast level are not evaluable for the 40T method. For the other spheres, the 40T method grossly overestimates the volumes and the segmented volumes are highly dependent on the statistical variations. These volumes are the least reproducible (80%) with a mean Dice Similarity Coefficient (DSC) of 0.67 and 90% classification error (CE). The other three methods reduce the dependency on noise and contrast in a similar manner by providing low bias (<10%) and CE (<25%) as well as a high DSC (0.88) and reproducibility (30%) for objects >17mm in diameter. However, for the smallest three spheres at a 2:1 contrast level, the performances of all three methods were significantly low, with the adaptive method being superior to the FCM and C-V (mean bias 168% and 350%, mean DSC 0.65 and 0.50, mean CE 227% and 454% for the adaptive and other two methods (approximately similar for FCM and C-V), respectively).ConclusionsThe segmentation accuracy of the fixed threshold-based method depends on size, contrast and noise. The intensity thresholds determined by the adaptive threshold methods are less sensitive to noise and therefore, the segmented volumes are more reproducible across different acquisition durations. A similar performance can be achieved with the FCM and C-V methods. Though, for small lesions (< 2cm diameter) with low counts and contrast, the adaptive threshold-based method outperforms the FCM and C-V methods, and the performance of neither of these methods is optimal for volumes <2cm in diameter. These three methods can only reliably be used to delineate tumours for diagnostic and monitoring purposes provided that the contrast between the tumour and background is not below a 2:1 ratio and the size of the tumour does not fall not below 2cm in diameter in response to treatment. They can also be used for different radiotracers with variable uptake. However, the FCM and C-V methods have the advantage of not requiring calibrations for different scanners and settings.