Prostate segmentation in transrectal ultrasound using magnetic resonance imaging priors

  • Qi ZengEmail author
  • Golnoosh Samei
  • Davood Karimi
  • Claudia Kesch
  • Sara S. Mahdavi
  • Purang Abolmaesumi
  • Septimiu E. Salcudean
Original Article



In the current standard of care, real-time transrectal ultrasound (TRUS) is commonly used for prostate brachytherapy guidance. As TRUS provides limited soft tissue contrast, segmenting the prostate gland in TRUS images is often challenging and subject to inter-observer and intra-observer variability, especially at the base and apex where the gland boundary is hard to define. Magnetic resonance imaging (MRI) has higher soft tissue contrast allowing the prostate to be contoured easily. In this paper, we aim to show that prostate segmentation in TRUS images informed by MRI priors can improve on prostate segmentation that relies only on TRUS images.


First, we compare the TRUS-based prostate segmentation used in the treatment of 598 patients with a high-quality MRI prostate atlas and observe inconsistencies at the apex and base. Second, motivated by this finding, we propose an alternative TRUS segmentation technique that is fully automatic and uses MRI priors. The algorithm uses a convolutional neural network to segment the prostate in TRUS images at mid-gland, where the gland boundary can be clearly seen. It then reconstructs the gland boundary at the apex and base with the aid of a statistical shape model built from an MRI atlas of 78 patients.


Compared to the clinical TRUS segmentation, our method achieves similar mid-gland segmentation results in the 598-patient database. For the seven patients who had both TRUS and MRI, our method achieved more accurate segmentation of the base and apex with the MRI segmentation used as ground truth.


Our results suggest that utilizing MRI priors in TRUS prostate segmentation could potentially improve the performance at base and apex.


Prostate segmentation Statistical shape model Magnetic resonance imaging prior Convolutional neural network 



This work was funded by Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), and the Prostate Cancer Canada (PCC). We would like to thank the support from the Charles Laszlo Chair in Biomedical Engineering held by Professor Salcudean. The authors also gratefully acknowledge the help from physicians and staff at the Vancouver Cancer Centre who have contributed to this project.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada
  2. 2.Vancouver Prostate CentreUniversity of British ColumbiaVancouverCanada
  3. 3.British Columbia Cancer AgencyVancouverCanada

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