Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.
Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm3in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.
Baines Imaging Research Laboratory, London Regional Cancer Program, A3-123A, 790 Commissioners Rd E, London, ON, N6A 4L6, Canada. mshahedi@uwo.ca.|Robarts Research Institute, The University of Western Ontario, London, ON, Canada. mshahedi@uwo.ca.|Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada. mshahedi@uwo.ca.
Maysam
http://orcid.org/0000-0001-9108-2010
M
Shahedi
Robarts Research Institute, The University of Western Ontario, London, ON, Canada.|The Department of Medical Imaging, The University of Western Ontario, London, ON, Canada.
Derek W
DW
Cool
Baines Imaging Research Laboratory, London Regional Cancer Program, A3-123A, 790 Commissioners Rd E, London, ON, N6A 4L6, Canada.|The Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.|The Department of Oncology, The University of Western Ontario, London, ON, Canada.
Glenn S
GS
Bauman
The Department of Medical Imaging, The University of Western Ontario, London, ON, Canada.
Matthew
M
Bastian-Jordan
Robarts Research Institute, The University of Western Ontario, London, ON, Canada.|Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada.|The Department of Medical Imaging, The University of Western Ontario, London, ON, Canada.|The Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
Aaron
A
Fenster
Baines Imaging Research Laboratory, London Regional Cancer Program, A3-123A, 790 Commissioners Rd E, London, ON, N6A 4L6, Canada.|Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada.|The Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.|The Department of Oncology, The University of Western Ontario, London, ON, Canada.
Aaron D
AD
Ward
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 / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results
3D segmentation; Automatic segmentation; Endorectal receive coil; Image segmentation; Magnetic resonance imaging; Validation