title: Image segmentation in treatment planning for prostate cancer using the region growing technique.
abstract: The purpose of this study was to evaluate the performance of a region growing technique for segmenting prostate, bladder and rectum in CT images of prostate cancer patients. Prostate, bladder and rectum were segmented in all CT images of 10 patients using the region growing technique and manual tracing. Volumes of the above organs computed with the region growing technique were compared with those from manually traced images on a slice-by-slice basis. Measurement reproducibility of both segmentation techniques was evaluated using the data obtained from four independent observers. The region growing technique was 1.5 times faster than manual tracing. There was no statistical difference between the slice volumes of prostate, bladder and rectum obtained by the two segmentation techniques (p > 0.05, paired Student's t-test). Correlation between slice volumes of all organs of interest provided both by region growing and by manual tracing was very good (prostate r2 = 0.84; bladder r2 = 0.93; rectum r2 = 0.85). An overall reasonable agreement was found between the two segmentation techniques. The intraobserver and interobserver variations for prostate, bladder and rectum volume segmentation were found to be lower with the region growing technique than with manual tracing. The suggested semi-automatic technique allows the possibility of generating accurate and reproducible segmentation of prostate, bladder and rectum from CT data with great saving in labour.
authors.affiliation: Department of Medical Physics, University Hospital of Iraklion, PO Box 1352, Greece.
authors.forename: M
authors.initials: M
authors.lastname: Mazonakis
authors.forename: J
authors.initials: J
authors.lastname: Damilakis
authors.forename: H
authors.initials: H
authors.lastname: Varveris
authors.forename: P
authors.initials: P
authors.lastname: Prassopoulos
authors.forename: N
authors.initials: N
authors.lastname: Gourtsoyiannis
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008297:Male; D015588:Observer Variation; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D012007:Rectum / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods; D001743:Urinary Bladder / Q000000981:diagnostic imaging