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The purpose of this study was to evaluate the use of a semiautomatic algorithm to simultaneously segment multiple high-dose-rate (HDR) gynecologic interstitial brachytherapy (ISBT) needles in three-dimensional (3D) transvaginal ultrasound (TVUS) images, with the aim of providing a clinically useful tool for intraoperative implant assessment.A needle segmentation algorithm previously developed for HDR prostate brachytherapy was adapted and extended to 3D TVUS images from gynecologic ISBT patients with vaginal tumors. Two patients were used for refining/validating the modified algorithm and five patients (8-12 needles/patient) were reserved as an unseen test data set. The images were filtered to enhance needle edges, using intensity peaks to generate feature points, and leveraged the randomized 3D Hough transform to identify candidate needle trajectories. Algorithmic segmentations were compared against manual segmentations and calculated dwell positions were evaluated.All 50 test data set needles were successfully segmented with 96% of algorithmically segmented needles having angular differences3° compared with manually segmented needles and the maximum Euclidean distance was2.1 mm. The median distance between corresponding dwell positions was 0.77 mm with 86% of needles having maximum differences3 mm. The mean segmentation time using the algorithm was30 s/patient.We successfully segmented multiple needles simultaneously in intraoperative 3D TVUS images from gynecologic HDR-ISBT patients with vaginal tumors and demonstrated the robustness of the algorithmic approach to image artifacts. This method provided accurate segmentations within a clinically efficient timeframe, providing the potential to be translated into intraoperative clinical use for implant assessment.

Purpose To demonstrate that manual prostate segmentation in transrectal ultrasound images can be replaced with semiautomatic segmentation. Methods and Materials Semiautomatic segmentation using a tapered ellipsoid model was applied to transrectal ultrasound images. Region-based volumetric evaluation was performed between original and physician-reviewed semiautomatic contours. For dosimetric assessment, treatment plans generated on semiautomatic contours were overlaid on physician-reviewed semiautomatic contours and dose parameters were computed. To establish a threshold for the acceptable amount of dosimetric degradation below which the adoption of semiautomatic planning is unacceptable, the range of variability in dosimetric quality attributed to manual variability was obtained and compared with that of semiautomatic contours. Results An average volume error (1—Dice similarity coefficient) of less than 7% between semiautomatic and manual volumes (140 cases) was obtained. The difference between the mean V100 of plans created for semiautomatic contours then overlaid on physician-reviewed semiautomatic contours and the original V100 values, that is, before overlaying on the physician-reviewed contours (41 cases) was lower than 5%. An average total duration of 2–4 min, which includes algorithm initialization, 11.67 ± 3.57 s algorithm time, and contour modification is required per case. This algorithm is being used at the British Columbia Cancer Agency and to this date has been applied for the treatment of more than 600 patients. Conclusions In terms of volumetric and dosimetric accuracy, the proposed algorithm is a suitable replacement for manual segmentation in the context of our planning technique. The benefits are shorter segmentation times; greater consistency; less reliance on user experience; and smooth, symmetric contours.