IEEE Trans Med Imaging. 2018 Jan;37(1):81-92. doi: 10.1109/TMI.2017.2739110. Epub 2017 Aug 11.
Accurate identification of the needle target is crucial for effective epidural anesthesia. Currently, epidural needle placement is administered by a manual technique, relying on the sense of feel, which has a significant failure rate. Moreover, misleading the needle may lead to inadequate anesthesia, post dural puncture headaches, and other potential complications. Ultrasound offers guidance to the physician for identification of the needle target, but accurate interpretation and localization remain challenges. A hybrid machine learning system is proposed to automatically localize the needle target for epidural needle placement in ultrasound images of the spine. In particular, a deep network architecture along with a feature augmentation technique is proposed for automatic identification of the anatomical landmarks of the epidural space in ultrasound images. Experimental results of the target localization on planes of 3-D as well as 2-D images have been compared against an expert sonographer. When compared with the expert annotations, the average lateral and vertical errors on the planes of 3-D test data were 1 and 0.4 mm, respectively. On 2-D test data set, an average lateral error of 1.7 mm and vertical error of 0.8 mm were acquired.
准确识别针尖目标对于有效实施硬膜外麻醉至关重要。目前，硬膜外针的放置是通过手动技术进行的，依赖于触感，但其失败率很高。此外，误导针尖可能导致麻醉不足、硬膜后穿刺头痛和其他潜在并发症。超声为医生提供了识别针尖目标的指导，但准确的解释和定位仍然是挑战。提出了一种混合机器学习系统，用于自动定位脊柱超声图像中的硬膜外针放置的针尖目标。特别是，提出了一种深度网络架构和特征增强技术，用于自动识别超声图像中硬膜外空间的解剖标志。目标定位在三维和二维图像的平面上的实验结果已经与专家超声师进行了比较。与专家注释相比，三维测试数据平面上的平均横向和垂直误差分别为 1mm 和 0.4mm。在二维测试数据集上，获得了 1.7mm 的平均横向误差和 0.8mm 的垂直误差。
