Literature DB >> 27668998

Evaluation and comparison of current biopsy needle localization and tracking methods using 3D ultrasound.

Yue Zhao1, Yi Shen2, Adeline Bernard3, Christian Cachard3, Hervé Liebgott4.   

Abstract

This article compares four different biopsy needle localization algorithms in both 3D and 4D situations to evaluate their accuracy and execution time. The localization algorithms were: Principle component analysis (PCA), random Hough transform (RHT), parallel integral projection (PIP) and ROI-RK (ROI based RANSAC and Kalman filter). To enhance the contrast of the biopsy needle and background tissue, a line filtering pre-processing step was implemented. To make the PCA, RHT and PIP algorithms comparable with the ROI-RK method, a region of interest (ROI) strategy was added. Simulated and ex-vivo data were used to evaluate the performance of the different biopsy needle localization algorithms. The resolutions of the sectorial and cylindrical volumes were 0.3mm×0.4mm×0.6mmand0.1mm×0.1mm×0.2mm (axial×lateral×azimuthal) respectively. In so far as the simulation and experimental results show, the ROI-RK method successfully located and tracked the biopsy needle in both 3D and 4D situations. The tip localization error was within 1.5mm and the axis accuracy was within 1.6mm. To the best of our knowledge, considering both localization accuracy and execution time, the ROI-RK was the most stable and time-saving method. Normally, accuracy comes at the expense of time. However, the ROI-RK method was able to locate the biopsy needle with high accuracy in real time, which makes it a promising method for clinical applications.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  3D ultrasound imaging; Needle localization; Region of interest; Ultrasound-guided intervention

Mesh:

Year:  2016        PMID: 27668998     DOI: 10.1016/j.ultras.2016.09.006

Source DB:  PubMed          Journal:  Ultrasonics        ISSN: 0041-624X            Impact factor:   2.890


  4 in total

1.  Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning.

Authors:  Yupei Zhang; Xiuxiu He; Zhen Tian; Jiwoong Jason Jeong; Yang Lei; Tonghe Wang; Qiulan Zeng; Ashesh B Jani; Walter J Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang
Journal:  IEEE Trans Med Imaging       Date:  2020-01-22       Impact factor: 10.048

2.  CASPER: computer-aided segmentation of imperceptible motion-a learning-based tracking of an invisible needle in ultrasound.

Authors:  Parmida Beigi; Robert Rohling; Septimiu E Salcudean; Gary C Ng
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-06-24       Impact factor: 2.924

3.  Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition.

Authors:  Yufang Dan; Jianwen Tao; Di Zhou
Journal:  Front Neurosci       Date:  2022-05-04       Impact factor: 5.152

4.  A 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images: a prospective randomized controlled trial.

Authors:  Weiwei Tang; Yun Zhou; Hui Zhao; Guangshun Sun; Dawei Rong; Zhitao Li; Meng Hu; Liu Han; Xu He; Suming Zhao; Xiaoyang Chen; Zhongming Li; Hongxin Yuan; Songwang Chen; Qian Wang; Zhouxiao Li; Jianping Gu; Xuehao Wang; Jinhua Song
Journal:  Ann Transl Med       Date:  2022-06
  4 in total

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