title: Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods.
abstract: OBJECTIVE
  Three-dimensional (3D) prostate image segmentation is useful for cancer diagnosis and therapy guidance, but can be time-consuming to perform manually and involves varying levels of difficulty and interoperator variability within the prostatic base, midgland (MG), and apex. In this study, the authors measured accuracy and interobserver variability in the segmentation of the prostate on T2-weighted endorectal magnetic resonance (MR) imaging within the whole gland (WG), and separately within the apex, midgland, and base regions.
  
  
  METHODS
  The authors collected MR images from 42 prostate cancer patients. Prostate border delineation was performed manually by one observer on all images and by two other observers on a subset of ten images. The authors used complementary boundary-, region-, and volume-based metrics [mean absolute distance (MAD), Dice similarity coefficient (DSC), recall rate, precision rate, and volume difference (ΔV)] to elucidate the different types of segmentation errors that they observed. Evaluation for expert manual and semiautomatic segmentation approaches was carried out. Compared to manual segmentation, the authors' semiautomatic approach reduces the necessary user interaction by only requiring an indication of the anteroposterior orientation of the prostate and the selection of prostate center points on the apex, base, and midgland slices. Based on these inputs, the algorithm identifies candidate prostate boundary points using learned boundary appearance characteristics and performs regularization based on learned prostate shape information.
  
  
  RESULTS
  The semiautomated algorithm required an average of 30 s of user interaction time (measured for nine operators) for each 3D prostate segmentation. The authors compared the segmentations from this method to manual segmentations in a single-operator (mean whole gland MAD = 2.0 mm, DSC = 82%, recall = 77%, precision = 88%, and ΔV = - 4.6 cm(3)) and multioperator study (mean whole gland MAD = 2.2 mm, DSC = 77%, recall = 72%, precision = 86%, and ΔV = - 4.0 cm(3)). These results compared favorably with observed differences between manual segmentations and a simultaneous truth and performance level estimation reference for this data set (whole gland differences as high as MAD = 3.1 mm, DSC = 78%, recall = 66%, precision = 77%, and ΔV = 15.5 cm(3)). The authors found that overall, midgland segmentation was more accurate and repeatable than the segmentation of the apex and base, with the base posing the greatest challenge.
  
  
  CONCLUSIONS
  The main conclusions of this study were that (1) the semiautomated approach reduced interobserver segmentation variability; (2) the segmentation accuracy of the semiautomated approach, as well as the accuracies of recently published methods from other groups, were within the range of observed expert variability in manual prostate segmentation; and (3) further efforts in the development of computer-assisted segmentation would be most productive if focused on improvement of segmentation accuracy and reduction of variability within the prostatic apex and base.
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada; Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 3K7, Canada; and Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Maysam
authors.initials: M
authors.lastname: Shahedi
authors.affiliation: Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 3K7, Canadaand The Department of Medical Imaging, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Derek W
authors.initials: DW
authors.lastname: Cool
authors.affiliation: The Department of Medical Imaging, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Cesare
authors.initials: C
authors.lastname: Romagnoli
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada; The Department of Medical Biophysics, The University of Western Ontario, London, Ontario N6A 3K7, Canada; and The Department of Oncology, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Glenn S
authors.initials: GS
authors.lastname: Bauman
authors.affiliation: The Department of Medical Imaging, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Matthew
authors.initials: M
authors.lastname: Bastian-Jordan
authors.affiliation: Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 3K7, Canada and Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Eli
authors.initials: E
authors.lastname: Gibson
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada and The Department of Oncology, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: George
authors.initials: G
authors.lastname: Rodrigues
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada and The Department of Oncology, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Belal
authors.initials: B
authors.lastname: Ahmad
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada and The Department of Oncology, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Lock
authors.affiliation: Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 3K7, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Ontario N6A 3K7, Canada; The Department of Medical Biophysics, The University of Western Ontario, London, Ontario N6A 3K7, Canada; and The Department of Medical Imaging, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.affiliation: London Regional Cancer Program, London, Ontario N6A 5W9, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Ontario N6A 3K7, Canada; The Department of Medical Biophysics, The University of Western Ontario, London, Ontario N6A 3K7, Canada; and The Department of Oncology, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
authors.forename: Aaron D
authors.initials: AD
authors.lastname: Ward
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012984:Software