William Thomas Hrinivich1,2, Douglas A Hoover1,3,4, Kathleen Surry1,3,4, Chandima Edirisinghe2, Jacques Montreuil2, David D'Souza3,4, Aaron Fenster1,2,3,5, Eugene Wong1,3,4,5. 1. Department of Medical Biophysics, University of Western Ontario, London, Ontario, N6A 5C1, Canada. 2. Imaging Research Laboratories, Robarts Research Institute, University of Western Ontario, London, Ontario, N6A 5K8, Canada. 3. Department of Oncology, University of Western Ontario, London, Ontario, N6A 4L6, Canada. 4. London Regional Cancer Program, London, Ontario, N6A 5W9, Canada. 5. Department of Physics and Astronomy, University of Western Ontario, London, Ontario, N6A 3K7, Canada.
Abstract
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 intraoperatively while the patient is anesthetized to create a treatment plan. The purpose of this article was 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 interneedle 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 modeling curvilinear trajectories.
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 intraoperatively while the patient is anesthetized to create a treatment plan. The purpose of this article was 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 interneedle 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 cancerpatients 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 modeling curvilinear trajectories.
Authors: Paolo Zaffino; Guillaume Pernelle; Andre Mastmeyer; Alireza Mehrtash; Hongtao Zhang; Ron Kikinis; Tina Kapur; Maria Francesca Spadea Journal: Phys Med Biol Date: 2019-08-14 Impact factor: 3.609
Authors: Alireza Mehrtash; Mohsen Ghafoorian; Guillaume Pernelle; Alireza Ziaei; Friso G Heslinga; Kemal Tuncali; Andriy Fedorov; Ron Kikinis; Clare M Tempany; William M Wells; Purang Abolmaesumi; Tina Kapur Journal: IEEE Trans Med Imaging Date: 2018-10-18 Impact factor: 10.048