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A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients
Author(s) -
Day Ellen,
Betler James,
Parda David,
Reitz Bodo,
Kirichenko Alexander,
Mohammadi Seyed,
Miften Moyed
Publication year - 2009
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 - 10.1118/1.3213099
Subject(s) - standard deviation , segmentation , nuclear medicine , imaging phantom , pixel , standardized uptake value , image segmentation , mathematics , anal cancer , radiation treatment planning , partial volume , volume (thermodynamics) , positron emission tomography , medicine , radiation therapy , computer science , cancer , physics , artificial intelligence , radiology , statistics , quantum mechanics
The application of automated segmentation methods for tumor delineation onF18‐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(SUV 2.5) , and (iii) mathematical technique based on a confidence connected region growing (CCRG) method. A phantom study was performed to determine theSUV % maxthreshold value and found to be 43%,SUV 43 % 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(PET avid)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 theSUV 43 % maxmethod, the volumes were always smaller than thePET avidvolume by a mean of 56% and a range of 21%–79%. The volumes from theSUV 2.5method were either smaller or larger than thePET avidvolume 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.