Share Email Print

Proceedings Paper

Automatic needle localization in intraoperative 3D transvaginal ultrasound images for high-dose-rate interstitial gynecologic brachytherapy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

High-dose-rate interstitial gynecologic brachytherapy requires multiple needles to be inserted into the tumor and surrounding area, avoiding nearby healthy organs-at-risk (OARs), including the bladder and rectum. We propose the use of a 360° three-dimensional (3D) transvaginal ultrasound (TVUS) guidance system for visualization of needles and report on the implementation of two automatic needle segmentation algorithms to aid the localization of needles intraoperatively. Two-dimensional (2D) needle segmentation, allowing for immediate adjustments to needle trajectories to mitigate needle deflection and avoid OARs, was implemented in near real-time using a method based on a convolutional neural network with a U-Net architecture trained on a dataset of 2D ultrasound images from multiple applications with needle-like structures. In 18 unseen TVUS images, the median position difference [95% confidence interval] was 0.27 [0.20, 0.68] mm and mean angular difference was 0.50 [0.27, 1.16]° between manually and algorithmically segmented needles. Automatic needle segmentation was performed in 3D TVUS images using an algorithm leveraging the randomized 3D Hough transform. All needles were accurately localized in a proof-of-concept image with a median position difference of 0.79 [0.62, 0.93] mm and median angular difference of 0.46 [0.31, 0.62]°, when compared to manual segmentations. Further investigation into the robustness of the algorithm to complex cases containing large shadowing, air, or reverberation artefacts is ongoing. Intraoperative automatic needle segmentation in interstitial gynecologic brachytherapy has the potential to improve implant quality and provides the potential for 3D ultrasound to be used for treatment planning, eliminating the requirement for post-insertion CT scans.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150K (16 March 2020); doi: 10.1117/12.2549664
Show Author Affiliations
Jessica R. Rodgers, The Univ. of Western Ontario (Canada)
Derek J. Gillies, The Univ. of Western Ontario (Canada)
W. Thomas Hrinivich, The Univ. of Western Ontario (Canada)
Igor Gyackov, The Univ. of Western Ontario (Canada)
Aaron Fenster, The Univ. of Western Ontario (Canada)

Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?