超声学家
硬膜外腔
超声波
医学
鉴定(生物学)
计算机科学
特征(语言学)
人工智能
数据集
计算机视觉
放射科
外科
语言学
植物
生物
哲学
作者
Mehran Pesteie,Victoria A. Lessoway,Purang Abolmaesumi,Robert Rohling
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2018-01-01
卷期号:37 (1): 81-92
被引量:51
标识
DOI:10.1109/tmi.2017.2739110
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI