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首页> 外文期刊>IEEE Transactions on Medical Imaging >Ultrasound Aided Vertebral Level Localization for Lumbar Surgery
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Ultrasound Aided Vertebral Level Localization for Lumbar Surgery

机译：腰椎手术的超声辅助椎体水平定位

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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.
机译：在腰椎疝气手术中正确定位椎骨水平以进行手术进入并非易事。在本文中，我们开发和评估一种在手术室（OR）中使用徒手二维超声（US）成像的解决方案。我们的系统利用了脊椎棘突形状的差异。术前勾勒出棘突，并在患者的腰椎X线片上标出。然后，在OR中，将棘突用二维矢状US成像，并自动进行分割并与X射线形​​状对齐。经过少量扫描的椎骨后，系统会稳健地匹配形状，并将X射线标签传播到US图像。我们工作的主要贡献是：我们从美国成像技术中提出了一种基于深度卷积神经网络的骨骼分割算法，该算法在性能和速度上均优于最新方法。我们提出了一种匹配策略，可确定要成像的脊突的水平。最后，我们评估了来自两家医院和两名观察员的19个临床数据集的完整程序。最终标记在92％的病例中是正确的，证明了基于US的脊柱外科手术进入点检测的可行性。

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