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Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging

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Abstract

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 cm3 in 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.

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Acknowledgements

The authors gratefully acknowledge the late Dr. Cesare Romagnoli for his support and scientific contribution to this work.

This work was supported by the Ontario Institute for Cancer Research and the Ontario Research Fund. This work was also supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation—Grant # RS2015-04. A. Fenster holds a Canada Research Chair in Biomedical Engineering and acknowledges the support of the Canada Research Chair Program. A. D. Ward holds a Cancer Care Ontario Research Chair in Cancer Imaging.

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Authors and Affiliations

  1. Baines Imaging Research Laboratory, London Regional Cancer Program, A3-123A, 790 Commissioners Rd E, London, ON, N6A 4L6, Canada

    Maysam Shahedi, Glenn S. Bauman & Aaron D. Ward

  2. Robarts Research Institute, The University of Western Ontario, London, ON, Canada

    Maysam Shahedi, Derek W. Cool & Aaron Fenster

  3. Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada

    Maysam Shahedi, Aaron Fenster & Aaron D. Ward

  4. The Department of Medical Imaging, The University of Western Ontario, London, ON, Canada

    Derek W. Cool, Matthew Bastian-Jordan & Aaron Fenster

  5. The Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada

    Glenn S. Bauman, Aaron Fenster & Aaron D. Ward

  6. The Department of Oncology, The University of Western Ontario, London, ON, Canada

    Glenn S. Bauman & Aaron D. Ward

Authors
  1. Maysam Shahedi
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  2. Derek W. Cool
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  3. Glenn S. Bauman
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  4. Matthew Bastian-Jordan
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  5. Aaron Fenster
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  6. Aaron D. Ward
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Correspondence to Maysam Shahedi.

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The study was approved by the research ethics board of our institution, and written informed consent was obtained from all patients prior to enrolment.

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Shahedi, M., Cool, D.W., Bauman, G.S. et al. Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging. J Digit Imaging 30, 782–795 (2017). https://doi.org/10.1007/s10278-017-9964-7

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  • DOI: https://doi.org/10.1007/s10278-017-9964-7

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