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A random walk‐based segmentation framework for 3D ultrasound images of the prostate
Author(s) -
Ma Ling,
Guo Rongrong,
Tian Zhiqiang,
Fei Baowei
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12396
Subject(s) - random walk , segmentation , computer vision , image segmentation , medical imaging , artificial intelligence , computer science , ultrasound , medical physics , medicine , radiology , mathematics , statistics
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.