IEEE Trans Med Imaging. 2017 Oct;36(10):2138-2147. doi: 10.1109/TMI.2017.2738612. Epub 2017 Aug 10.
Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes of the vertebrae. The spinous processes are pre-operatively outlined and labeled in a lateral lumbar X-ray of the patient. Then, in the OR the spinous processes are imaged with 2-D sagittal US, and are automatically segmented and registered with the X-ray shapes. After a small number of scanned vertebrae, the system robustly matches the shapes, and propagates the X-ray label to the US images. The main contributions of our work are: we propose a deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed. We present a matching strategy that determines the levels of the spinal processes being imaged. And lastly, we evaluate the complete procedure on 19 clinical data sets from two hospitals, and two observers. The final labeling was correct in 92% of the cases, demonstrating the feasibility of US-based surgical entry point detection for spinal surgeries.
在腰椎疝手术中，准确定位手术入路的正确椎骨并不容易。在本文中，我们开发并评估了一种在手术室（OR）中使用徒手 2-D 超声（US）成像的解决方案。我们的系统利用了椎骨棘突形状的差异。在患者的侧位腰椎 X 光片上，对棘突进行术前勾勒和标记。然后，在 OR 中，用 2-D 矢状 US 对棘突进行成像，并自动对其进行分割和与 X 射线形状进行配准。在扫描了少量椎骨后，系统可以稳健地匹配形状，并将 X 射线标签传播到 US 图像上。我们工作的主要贡献是：我们提出了一种基于深度卷积神经网络的从 US 成像中进行骨分割的算法，该算法在性能和速度方面均优于现有方法。我们提出了一种匹配策略，用于确定正在成像的棘突的水平。最后，我们在来自两家医院的 19 个临床数据集和两名观察者上评估了整个过程。最终的标记在 92%的病例中是正确的，这证明了基于 US 的脊柱手术入路检测在手术中的可行性。
