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SU‐F‐J‐95: Impact of Shape Complexity On the Accuracy of Gradient‐Based PET Volume Delineation
Author(s) -
Dance M,
Wu G,
Gao Y,
Pirozzi S,
Nelson A,
Das S,
Lian J
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4956003
Subject(s) - imaging phantom , segmentation , nuclear medicine , standard deviation , mathematics , metric (unit) , volume (thermodynamics) , artificial intelligence , computer science , medicine , physics , statistics , operations management , quantum mechanics , economics
Purpose: Explore correlation of tumor complexity shape with PET target volume accuracy when delineated with gradient‐based segmentation tool. Methods: A total of 24 clinically realistic digital PET Monte Carlo (MC) phantoms of NSCLC were used in the study. The phantom simulated 29 thoracic lesions (lung primary and mediastinal lymph nodes) of varying size, shape, location, and 18 F‐FDG activity. A program was developed to calculate a curvature vector along the outline and the standard deviation of this vector was used as a metric to quantify a shape's “complexity score”. This complexity score was calculated for standard geometric shapes and MC‐generated target volumes in PET phantom images. All lesions were contoured using a commercially available gradient‐based segmentation tool and the differences in volume from the MC‐generated volumes were calculated as the measure of the accuracy of segmentation. Results: The average absolute percent difference in volumes between the MC‐volumes and gradient‐based volumes was 11% (0.4%–48.4%). The complexity score showed strong correlation with standard geometric shapes. However, no relationship was found between the complexity score and the accuracy of segmentation by gradient‐based tool on MC simulated tumors (R 2 = 0.156). When the lesions were grouped into primary lung lesions and mediastinal/mediastinal adjacent lesions, the average absolute percent difference in volumes were 6% and 29%, respectively. The former group is more isolated and the latter is more surround by tissues with relatively high SUV background. Conclusion: The complexity shape of NSCLC lesions has little effect on the accuracy of the gradient‐based segmentation method and thus is not a good predictor of uncertainty in target volume delineation. Location of lesion within a relatively high SUV background may play a more significant role in the accuracy of gradient‐based segmentation.