Iterative threshold segmentation for PET target volume delineation.
The purpose of this work is to create a rigorous method of segmenting PET images using an automated iterative technique. To this end a phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast between target and background. Numerous target to background activity concentration ratios were investigated but found to have minimal effect in comparison to the influence of target size. Functions were fit to this data and used to construct an iterative threshold segmentation algorithm. In all cases this approach yielded convergence within ten iterations. Iterative threshold segmentation was applied using both an axial and tri-axial approach to the spherical targets and also to two irregularly shaped volumes. Of these two approaches, the tri-axial method proved less susceptible to image noise and better at dealing with partial volume effects at the interface between target and background. For comparative purposes, single thresholds of 28% and 40% were also applied to the spherical data sets. The tri-axial iterative method was found capable of delineating cross sections with areas greater than 250 mm2 to within the maximum resolution possible (1 pixel width). Cross sections of less than 250 mm2 in area were resolved by the tri-axial method to within 2 pixel widths of their true physical extent. Local contrast based iterative threshold segmentation shows promise as a method of rigorously delineating PET target volumes with good accuracy subject to the limitations imposed by the image resolution which currently characterizes this modality.
Drever
Laura
L
Department of Medical Physics, BC Cancer Agency, Victoria, British Columbia V8R 6V5, Canada.
Roa
Wilson
W
McEwan
Alexander
A
Robinson
Don
D
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity