Share Email Print
cover

Proceedings Paper

Weakly supervised multi-needle detection in 3D ultrasound images with bidirectional convolutional sparse coding
Author(s): Yupei Zhang; Joseph Harms; Yang Lei; Tonghe Wang; Tian Liu; Ashesh B. Jani; Walter J. Curran; Pretesh Patel; Xiaofeng Yang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided prostate high dose rate (HDR) brachytherapy. In this paper, we propose a workflow for multineedle detection in 3D ultrasound (US) images with corresponding CT images used for supervision. Since the CT images do not exactly match US images, we propose a novel sparse model, dubbed Bidirectional Convolutional Sparse Coding (BiCSC), to tackle this weakly supervised problem. BiCSC aims to extract the latent features from US and CT and then formulate a relationship between them where the learned features from US yield to the features from CT. Resultant images allow for clear visualization of the needle while reducing image noise and artifacts. On the reconstructed US images, a clustering algorithm is employed to find the cluster centers which correspond to the true needle position. Finally, the random sample consensus algorithm (RANSAC) is used to model a needle per ROI. Experiments are conducted on prostate image datasets from 10 patients. Visualization and quantitative results show the efficacy of our proposed workflow. This learning-based technique could provide accurate needle detection for US-guided high-dose-rate prostate brachytherapy, and further enhance the clinical workflow for prostate HDR brachytherapy.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 1131914 (16 March 2020);
Show Author Affiliations
Yupei Zhang, Winship Cancer Institute of Emory Univ. (United States)
Joseph Harms, Winship Cancer Institute of Emory Univ. (United States)
Yang Lei, Winship Cancer Institute of Emory Univ. (United States)
Tonghe Wang, Winship Cancer Institute of Emory Univ. (United States)
Tian Liu, Winship Cancer Institute of Emory Univ. (United States)
Ashesh B. Jani, Winship Cancer Institute of Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute of Emory Univ. (United States)
Pretesh Patel, Winship Cancer Institute of Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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