Random walk based segmentation for the prostate on 3D transrectal ultrasound images

Ling Ma; Rongrong Guo; Zhiqiang Tian; Rajesh Venkataraman; Saradwata Sarkar; Xiabi Liu; Peter T. Nieh; Viraj V. Master; David M. Schuster; Baowei Fei

doi:10.1117/12.2216526

18 March 2016 Random walk based segmentation for the prostate on 3D transrectal ultrasound images

Ling Ma, Rongrong Guo, Zhiqiang Tian, Rajesh Venkataraman, Saradwata Sarkar, Xiabi Liu, Peter T. Nieh, Viraj V. Master, David M. Schuster, Baowei Fei

Author Affiliations +

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 978607 (2016) https://doi.org/10.1117/12.2216526
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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.

Citation Download Citation

Ling Ma, Rongrong Guo, Zhiqiang Tian, Rajesh Venkataraman, Saradwata Sarkar, Xiabi Liu, Peter T. Nieh, Viraj V. Master, David M. Schuster, and Baowei Fei "Random walk based segmentation for the prostate on 3D transrectal ultrasound images", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978607 (18 March 2016); https://doi.org/10.1117/12.2216526

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