Beigi Parmida, Rohling Robert, Salcudean Tim, Lessoway Victoria A, Ng Gary C
Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada.
Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada; Mechanical Engineering Department, University of British Columbia, Vancouver, BC, Canada.
Ultrasonics. 2017 Jul;78:18-22. doi: 10.1016/j.ultras.2017.02.010. Epub 2017 Feb 16.
We propose a novel learning-based approach to detect an imperceptible hand-held needle in ultrasound images using the natural tremor motion. The minute tremor induced on the needle however is also transferred to the tissue in contact with the needle, making the accurate needle detection a challenging task. The proposed learning-based framework is based on temporal analysis of the phase variations of pixels to classify them according to the motion characteristics. In addition to the classification, we also obtain a probability map of the segmented pixels by cross-validation. A Hough transform is then used on the probability map to localize the needle using the segmented needle and posterior probability estimate. The two-step probability-weighted localization on the segmented needle in a learning framework is the key innovation which results in localization improvement and adaptability to specific clinical applications. The method was tested in vivo for a standard 17 gauge needle inserted at 50-80° insertion angles and 40-60mm depths. The results showed an average accuracy of (2.12°, 1.69mm) and 81%±4% for localization and classification, respectively.
我们提出了一种新颖的基于学习的方法，利用自然震颤运动在超声图像中检测难以察觉的手持针。然而，针上产生的微小震颤也会传递到与针接触的组织上，这使得准确的针检测成为一项具有挑战性的任务。所提出的基于学习的框架基于对像素相位变化的时间分析，根据运动特征对其进行分类。除了分类之外，我们还通过交叉验证获得了分割像素的概率图。然后在概率图上使用霍夫变换，利用分割出的针和后验概率估计来定位针。在学习框架中对分割出的针进行两步概率加权定位是关键创新点，这带来了定位精度的提高以及对特定临床应用的适应性。该方法在体内针对以50 - 80°插入角度和40 - 60mm深度插入的标准17号针进行了测试。结果表明，定位和分类的平均准确率分别为(2.12°, 1.69mm)和81%±4%。
