Simultaneous automatic segmentation of multiple needles using 3D ultrasound for high‐dose‐rate prostate brachytherapy

Purpose Sagittally-reconstructed 3D (SR3D) ultrasound imaging shows promise for improved needle localization for high-dose-rate prostate brachytherapy (HDR-BT); however, needles must be manually segmented intra-operatively while the patient is anesthetized to create a treatment plan. The purpose of this article is to describe and validate an automatic needle segmentation algorithm designed for HDR-BT, specifically capable of simultaneously segmenting all needles in an HDR-BT implant using a single SR3D image with ~5 mm inter-needle spacing. Materials and Methods The segmentation algorithm involves regularized feature-point classification and line trajectory identification based on the randomized 3D Hough transform modified to handle multiple straight needles in a single image simultaneously. Needle tips are identified based on peaks in the derivative of the signal intensity profile along the needle trajectory. For algorithm validation, 12 prostate cancer patients underwent HDR-BT during which SR3D images were acquired with all needles in place. Needles present in each of the 12 images were segmented manually, providing a gold-standard for comparison, and using the algorithm. Tip errors were assessed in terms of the 3D Euclidean distance between needle tips, and trajectory error was assessed in terms of 2D distance in the axial plane and angular deviation between trajectories. Results In total, 190 needles were investigated. Mean execution time of the algorithm was 11.0 s per patient, or 0.7 s per needle. The algorithm identified 82% and 85% of needle tips with 3D errors ≤3 mm and ≤5 mm respectively, 91% of needle trajectories with 2D errors in the axial plane ≤3 mm, and 83% of needle trajectories with angular errors ≤3°. The largest tip error component was in the needle insertion direction. Conclusions Previous work has indicated HDR-BT needles may be manually segmented using SR3D images with insertion depth errors ≤3 mm and ≤5 mm for 83% and 92% of needles respectively. The algorithm shows promise for reducing the time required for the segmentation of straight HDR-BT needles, and future work involves improving needle tip localization performance through improved image quality and modelling curvilinear trajectories. This article is protected by copyright. All rights reserved.