title: Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images.
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.
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; School of Computer Science, Beijing Institute of Technology, Beijing.
authors.forename: Ling
authors.initials: L
authors.lastname: Ma
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
authors.forename: Rongrong
authors.initials: R
authors.lastname: Guo
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Tian
authors.affiliation: Department of R&D, Eigen, Grass Valley, CA.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Venkataraman
authors.affiliation: Department of R&D, Eigen, Grass Valley, CA.
authors.forename: Saradwata
authors.initials: S
authors.lastname: Sarkar
authors.affiliation: School of Computer Science, Beijing Institute of Technology, Beijing.
authors.forename: Xiabi
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department of Urology, Emory University School of Medicine, Atlanta, GA.
authors.forename: Peter T
authors.initials: PT
authors.lastname: Nieh
authors.affiliation: Department of Urology, Emory University School of Medicine, Atlanta, GA.
authors.forename: Viraj V
authors.initials: VV
authors.lastname: Master
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
authors.forename: David M
authors.initials: DM
authors.lastname: Schuster
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
authors.forename: Baowei
authors.initials: B
authors.lastname: Fei
keywords: 3D transrectal ultrasound image (TRUS); decision tree; prostate segmentation; random walk; semi-automatic segmentation