Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Department of Ultrasound, Womens Hospital, Vancouver, British Columbia, Canada.
Ultrasound Med Biol. 2020 Oct;46(10):2846-2854. doi: 10.1016/j.ultrasmedbio.2020.04.018. Epub 2020 Jul 7.
Effective epidural needle placement and injection involves accurate identification of the midline of the spine. Ultrasound, as a safe pre-procedural imaging modality, is suitable for epidural guidance because it offers adequate visibility of the vertebral anatomy. However, image interpretation remains a key challenge, especially for novices. A deep neural network is proposed to automatically classify the transverse ultrasound images of the vertebrae and identify the midline. To distinguish midline images from off-center frames, the proposed network detects the left-right symmetric anatomic landmarks. To assess the feasibility of the proposed method for midline detection, a data set of ultrasound images was collected from 20 volunteers, whose body mass indices were less than 30. The data were split into two segments, for training and test. The performance of the proposed method was further evaluated using fourfold cross validation. Moreover, it was compared against a state-of-the-art deep neural network. Compared with the gold standard provided by an expert sonographer, the proposed trained network correctly classified 88% of the transverse planes from unseen test patients. This capability supports the first step of guiding the placement of an epidural needle.
有效的硬膜外针置管和注射需要准确识别脊柱的中线。超声作为一种安全的术前成像方式，非常适合用于硬膜外引导，因为它可以充分显示椎体解剖结构。然而，图像解释仍然是一个关键挑战，特别是对于新手来说。我们提出了一种深度神经网络，可以自动对椎体的横向超声图像进行分类，并识别中线。为了区分中线图像和非中心图像，所提出的网络检测左右对称的解剖标志点。为了评估所提出的中线检测方法的可行性，我们从 20 名体重指数小于 30 的志愿者中收集了一组超声图像数据集。这些数据被分为训练集和测试集。我们还使用四折交叉验证进一步评估了所提出方法的性能。此外，我们还将其与最先进的深度神经网络进行了比较。与专家超声医师提供的金标准相比，经过训练的网络正确分类了来自未见过的测试患者的 88%的横向平面。这一能力支持引导硬膜外针放置的第一步。
