title: A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours.
abstract: To more accurately and precisely delineate a tumor in a 3D PET image, we proposed a novel, semi-automatic, two-stage method by utilizing an adaptive region-growing algorithm and a dual-front active contour model. First, a rough region of interest (ROI) is manually drawn by a radiation oncologist that encloses a tumor. The voxel having the highest intensity in the ROI is chosen as a seed point. An adaptive region growing algorithm successively appends to the seed point all neighboring voxels whose intensities > = T of the mean of the current region. When T varies from 100% to 0%, a sharp volume increase, indicating the transition from the tumor to the background, always occurs at a certain T value. A preliminary tumor boundary is determined just before the sharp volume increase, which is found to be slightly outside of the known tumor in all tested phantoms. A novel dual-front active contour model utilizing region-based information is then applied to refine the preliminary boundary automatically. We tested the two-stage method on six spheres (0.5-20 ml) in a cylindrical container under different source to background ratios. Comparisons between the two-stage method and an iterative threshold method demonstrate its higher detection accuracy for small tumors (less than 6 ml). One patient study was tested and evaluated by two experienced radiation oncologists. The study illustrated that this two-stage method has several advantages. First, it does not require any threshold-volume curves, which are different and must be calibrated for each scanner and image reconstruction method. Second, it does not use any iso-threshold lines as contours. Third, the final result is reproducible and is independent of the manual rough ROIs. Fourth, this method is an adaptive algorithm that can process different images automatically.
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.forename: Wade L
authors.initials: WL
authors.lastname: Thorstad
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Biehl
authors.forename: Richard
authors.initials: R
authors.lastname: Laforest
authors.forename: Yi
authors.initials: Y
authors.lastname: Su
authors.forename: Kooresh I
authors.initials: KI
authors.lastname: Shoghi
authors.forename: Eric D
authors.initials: ED
authors.lastname: Donnelly
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Low
authors.forename: Wei
authors.initials: W
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography
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