title: Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.
abstract: BACKGROUND
  This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial.
  
  
  METHODS
  CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets.
  
  
  RESULTS
  There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations < 0.4 cm across the majority of image slices). Although there was some variation in interpretation of the superior-inferior (cranio-caudal) extent of rectum, human-observer contours were typically within a mean 0.6 cm of automatically-defined contours. Prostate structures were more consistent for the HR case than the IR case with all human observers segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial.
  
  
  CONCLUSIONS
  This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset adds a significant systematic bias to the resulting volumes and their use in the context of a multicentre trial should be carefully considered.
authors: Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.
authors: John P
authors: JP
authors: Geraghty
authors: Garry
authors: G
authors: Grogan
authors: Martin A
authors: MA
authors: Ebert
mesh_terms: D000726:Androgen Antagonists / Q000008:administration & dosage; D059248:Chemoradiotherapy / Q000592:standards; D006801:Humans; D008297:Male; D015588:Observer Variation; D010388:Pelvis / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000532:radiotherapy*; D011785:Quality Assurance, Health Care; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods / Q000592:standards*; D016032:Randomized Controlled Trials as Topic / Q000592:standards*; D014057:Tomography, X-Ray Computed
chemical_list: D000726:Androgen Antagonists
references: Radiother Oncol. 2008 Apr;87(1):93-9
references: 18155791
references: Radiother Oncol. 1998 Jun;47(3):285-92
references: 9681892
references: Australas Phys Eng Sci Med. 2012 Jun;35(2):159-64
references: 22581501
references: IEEE Trans Image Process. 2012 Aug;21(8):3546-59
references: 22510948
references: Med Phys. 1999 Jun;26(6):931-40
references: 10436894
references: J Med Imaging Radiat Oncol. 2010 Oct;54(5):401-10
references: 20958937
references: Radiother Oncol. 2009 Dec;93(3):474-8
references: 19758720
references: Radiother Oncol. 2009 Mar;90(3):337-45
references: 18812252
references: Int J Radiat Oncol Biol Phys. 1999 Jan 1;43(1):57-66
references: 9989514
references: Radiother Oncol. 2009 Mar;90(3):285-90
references: 19038468
references: J Med Imaging Radiat Oncol. 2013 Apr;57(2):247-57
references: 23551788
references: Radiother Oncol. 2008 Feb;86(2):200-10
references: 18077034
references: Clin Oncol (R Coll Radiol). 2005 Sep;17(6):456-64
references: 16149290
references: Med Phys. 2012 Oct;39(10):6372-87
references: 23039673
references: Acta Oncol. 2011 May;50(4):539-46
references: 21391773
references: IEEE Trans Med Imaging. 2012 Oct;31(10):1955-64
references: 22875243