Volume 44, Issue 10 p. 5128-5142
Research Article

A random walk-based segmentation framework for 3D ultrasound images of the prostate

Ling Ma

Ling Ma

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30329 USA

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Rongrong Guo

Rongrong Guo

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30329 USA

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Zhiqiang Tian

Zhiqiang Tian

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30329 USA

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Baowei Fei

Corresponding Author

Baowei Fei

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30329 USA

The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30329 USA

Winship Cancer Institute of Emory University, Atlanta, GA, 30329 USA

Department of Mathematics and Computer Science, Emory College of Emory University, Atlanta, GA, 30329 USA

Author to whom correspondence should be addressed. Electronic mail: [email protected]; Telephone: 404-712-5649.Search for more papers by this author
First published: 05 June 2017
Citations: 3

Abstract

Purpose

Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images.

Methods

The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance.

Results

We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist.

Conclusions

The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.