Pubic arch detection in transrectal ultrasound guided prostate cancer therapy

被引:31
|
作者
Pathak, SD
Grimm, PD
Chalana, V
Kim, Y
机构
[1] Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA
[2] Univ Washington, Dept Bioengn, Seattle, WA 98195 USA
[3] Swedish Med Ctr, Seattle Prostate Inst, Seattle, WA 98122 USA
关键词
image segmentation; prostate brachytherapy; prostate cancer; recursive model fitting; sticks; transrectal ultrasound;
D O I
10.1109/42.736032
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
New biopsy techniques, increased life expectancy, and prostate-specific antigen (PSA) screening have contributed to an increase in the reported incidence of prostate cancer. Among several treatment options available to the patients, transperineal prostate brachytherapy has emerged as a medically successful, cost-effective outpatient procedure for treating localized prostate cancer. Transperineal prostate brachytherapy employs transrectal ultrasound (TRUS) as the primary imaging modality to accurately preplan and subsequently execute the placement of radioactive seeds into the prostate. Under TRUS guidance, a needle (preloaded with radioactive seeds) is inserted through a template guide, through the perineum and into a predetermined prostate target. The pubic arch, formed by the central union of pelvic bones, is a potential barrier to the passage of these needles in the prostate. A critical aspect, therefore, in the planning and execution of the brachytherapy procedure is the accurate assessment of pubic arch interference (PAI) in relation to the prostate. Traditionally, the evaluation of PAI has involved computed tomography correlate scanning or crude subjective evaluations. In this paper, we describe a new method of assessing PAI by detecting the pubic arch via image processing on the TRUS images. The PAI detection (PAID) algorithm first uses a technique known as sticks to selectively enhance the contrast of linear features in ultrasound images. Next, the enhanced image is thresholded via percentile thresholding. Finally, we fit a parabola (a model for the pubic arch) recursively to the thresholded image. Our evaluation result from 15 cases indicates that the algorithm can successfully detect the pubic arch with 90% accuracy. Based on this study, we believe that detecting the pubic arch and assessing PAI can be done practically and more accurately in the clinical setting using TRUS rather than the current available methods.
引用
收藏
页码:762 / 771
页数:10
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