Pesteie Mehran, Abolmaesumi Purang, Ashab Hussam Al-Deen, Lessoway Victoria A, Massey Simon, Gunka Vit, Rohling Robert N
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada,
Int J Comput Assist Radiol Surg. 2015 Jun;10(6):901-12. doi: 10.1007/s11548-015-1202-5. Epub 2015 Apr 23.
Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections.
A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification.
The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image.
A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for detecting the laminae and facet joints in ultrasound images has been proposed. The system has the potential to assist the anesthesiologists in quickly finding the target plane for epidural steroid injections and facet joint injections.
注射疗法是治疗背痛的常用方法。该操作通常涉及经皮将针插入椎骨之间或周围，以在神经束附近注射麻醉剂。最常见的情况是，脊柱注射要么通过触诊盲目进行，要么在荧光透视或计算机断层扫描引导下进行。最近，由于此类成像方式存在电离辐射的缺点，人们越来越有兴趣将超声成像作为替代方法。然而，脊柱解剖结构复杂，存在不同的波浪状结构，且受斑点噪声影响，使得准确识别合适的注射平面变得困难。本研究的目的是提出一种自动化系统，能够识别硬膜外类固醇注射和小关节注射的最佳平面。
提出了一种多尺度多方向特征提取系统，以实现对合适平面的自动识别。使用序列有序哈达玛变换在多个尺度上获取局部哈达玛系数。从与超声图像中不同区域对应的局部系数中提取方向特征。基于局部方向哈达玛特征训练人工神经网络进行分类。
所提出的方法产生了用于分类的独特特征，对于硬膜外类固醇注射，在1090张图像中成功分类了1032张；对于小关节注射，在1052张图像中成功分类了990张。为了验证所提出的方法，进行了留一法交叉验证。留一法验证的平均分类准确率对于硬膜外目标为94%，对于小关节目标为90%。此外，对于一幅原始二维超声图像，所提出方法的特征提取时间为20毫秒。
提出了一种基于序列有序哈达玛变换提取的局部方向哈达玛特征的实时机器学习系统，用于在超声图像中检测椎板和小关节。该系统有潜力协助麻醉医生快速找到硬膜外类固醇注射和小关节注射的目标平面。
