Effect of 3-D Automated Prostate Segmentation for Ultrasound Image Guided Repeat Biopsy Application

摘要

Prostate repeat biopsy has become one of the key requirements in today's prostate cancer detection. Urologists are interested in knowing previous 3-D biopsy locations during the current visit of the patient. Eigen has developed a system for performing 3-D Ultrasound image guided prostate biopsy. The repeat biopsy tool consists of three stages: (1) segmentation of the prostate capsules from previous and current ultrasound volumes; (2) registration of segmented surfaces using adaptive focus deformable model; (3) mapping of old biopsy sites onto new volume via thin-plate splines (TPS). The system critically depends on accurate 3-D segmentation of capsule volumes. In this paper, we study the effect of automated segmentation technique on the accuracy of 3-D ultrasound guided repeat biopsy. Our database consists of 38 prostate volumes of different patients which are acquired using Philips sidefire transrectal ultrasound (TRUS) probe. The prostate volumes were segmented in three ways: expert segmentation, semi-automated segmentation, and fully automated segmentation. New biopsy sites were identified in the new volumes from different segmentation methods, and we compared the mean squared distance between biopsy sites. It is demonstrated that the performance of our fully automated segmentation tool is comparable to that of semi-automated segmentation method.