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

• Type of Material: Article
• Authors: Ling Ma, Emory University; RongRong Guo, Emory University; Zhiqiang Tian, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2017-10-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 American Association of Physicists in Medicine.
• Final Published Version (URL): http://dx.doi.org/10.1002/mp.12396
• Title of Journal or Parent Work: Medical Physics
• ISSN: 0094-2405
• Volume: 44
• Issue: 10
• Start Page: 5128
• End Page: 5142
• Grant/Funding Information: This work was partially supported by NIH grants CA156775, CA176684, and CA204254, and Georgia Research Alliance Distinguished Scientists Award.
• Keywords: context; random walk; image segmentation; MODEL; CLASSIFICATION; prostate cancer; Science & Technology; Life Sciences & Biomedicine; Radiology, Nuclear Medicine & Medical Imaging; classification; transrectal ultrasound (TRUS); BOUNDARY SEGMENTATION
• Research Categories: Health Sciences, Radiology

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.