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.
Real-time knowledge of capsule volume of an organ provides a valuable clinical tool for 3D biopsy applications. It is
challenging to estimate this capsule volume in real-time due to the presence of speckles, shadow artifacts, partial volume
effect and patient motion during image scans, which are all inherent in medical ultrasound imaging.
The volumetric ultrasound prostate images are sliced in a rotational manner every three degrees. The automated
segmentation method employs a shape model, which is obtained from training data, to delineate the middle slices of
volumetric prostate images. Then a "DDC" algorithm is applied to the rest of the images with the initial contour
obtained. The volume of prostate is estimated with the segmentation results.
Our database consists of 36 prostate volumes which are acquired using a Philips ultrasound machine using a Side-fire
transrectal ultrasound (TRUS) probe. We compare our automated method with the semi-automated approach. The mean
volumes using the semi-automated and complete automated techniques were 35.16 cc and 34.86 cc, with the error of
7.3% and 7.6% compared to the volume obtained by the human estimated boundary (ideal boundary), respectively. The
overall system, which was developed using Microsoft Visual C++, is real-time and accurate.