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.

有效的硬膜外针头放置和注射涉及准确识别脊柱中线。超声作为一种安全的术前成像方式，适用于硬膜外引导，因为它提供了足够的椎体解剖结构可见性。然而，图像解读仍然是一个关键挑战，尤其是对于新手而言。提出了一种深度神经网络来自动分类椎骨的横向超声图像并识别中线。为了区分中线图像和偏离中心的帧，所提出的网络检测左右对称的解剖标志。为了评估所提出的中线检测方法的可行性，从 20 名体重指数小于 30 的志愿者中收集了一组超声图像。将数据分为两部分，用于训练和测试。使用四重交叉验证进一步评估了所提出方法的性能。此外，它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黄金标准相比，所提出的训练网络正确分类了 88% 来自看不见的测试患者的横向平面。此功能支持引导硬膜外针头放置的第一步。