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Author Notes:

E-mail: bfei@emory.edu; http://feilab.org.

The work was conducted in the Quantitative BioImaging Laboratory in the Emory Center for Systems Imaging (CSI) of Emory University School of Medicine.


Research Funding:

This research is supported in part by NIH grants (CA176684 and CA156775).

LM was partially supported by International Graduate Exchange Program of Beijing Institute of Technology.

XL was partially supported by National Natural Science Foundation of China (Grant no. 60973059, 81171407) and the Program for New Century Excellent Talents in Universities of China (Grant no. NCET-10-0044).


  • Science & Technology
  • Physical Sciences
  • Life Sciences & Biomedicine
  • Optics
  • Radiology, Nuclear Medicine & Medical Imaging
  • prostate segmentation
  • random walk
  • decision tree
  • semi-automatic segmentation
  • 3D transrectal ultrasound image (TRUS)

Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images


Proceedings Title:

Proceedings of SPIE

Conference Name:

Conference on SPIE Image-Guided Procedures, Robotic Interventions, and Modeling


Conference Place:

San Diego, CA


Volume 9786

Publication Date:

Type of Work:

Conference | Post-print: After Peer Review


This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.

Copyright information:

© 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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