title: A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients.
abstract: The application of automated segmentation methods for tumor delineation on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images presents an opportunity to reduce the interobserver variability in radiotherapy (RT) treatment planning. In this work, three segmentation methods were evaluated and compared for rectal and anal cancer patients: (i) Percentage of the maximum standardized uptake value (SUV% max), (ii) fixed SUV cutoff of 2.5 (SUV2.5), and (iii) mathematical technique based on a confidence connected region growing (CCRG) method. A phantom study was performed to determine the SUV% max threshold value and found to be 43%, SUV43% max. The CCRG method is an iterative scheme that relies on the use of statistics from a specified region in the tumor. The scheme is initialized by a subregion of pixels surrounding the maximum intensity pixel. The mean and standard deviation of this region are measured and the pixels connected to the region are included or not based on the criterion that they are greater than a value derived from the mean and standard deviation. The mean and standard deviation of this new region are then measured and the process repeats. FDG-PET-CT imaging studies for 18 patients who received RT were used to evaluate the segmentation methods. A PET avid (PETavid) region was manually segmented for each patient and the volume was then used to compare the calculated volumes along with the absolute mean difference and range for all methods. For the SUV43% max method, the volumes were always smaller than the PETavid volume by a mean of 56% and a range of 21%-79%. The volumes from the SUV2.5 method were either smaller or larger than the PETavid volume by a mean of 37% and a range of 2%-130%. The CCRG approach provided the best results with a mean difference of 9% and a range of 1%-27%. Results show that the CCRG technique can be used in the segmentation of tumor volumes on FDG-PET images, thus providing treatment planners with a clinically viable starting point for tumor delineation and minimizing the interobserver variability in radiotherapy planning.
authors.affiliation: Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, Pennsylvania 15212, USA.
authors.forename: Ellen
authors.initials: E
authors.lastname: Day
authors.forename: James
authors.initials: J
authors.lastname: Betler
authors.forename: David
authors.initials: D
authors.lastname: Parda
authors.forename: Bodo
authors.initials: B
authors.lastname: Reitz
authors.forename: Alexander
authors.initials: A
authors.lastname: Kirichenko
authors.forename: Seyed
authors.initials: S
authors.lastname: Mohammadi
authors.forename: Moyed
authors.initials: M
authors.lastname: Miften
mesh_terms: D000465:Algorithms; D001005:Anus Neoplasms / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18