Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < $0.5$ mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.

中文翻译：

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在导航脊柱手术中实现无标记脊柱跟踪的光学成像。


手术导航系统越来越多地用于复杂的脊柱手术，以避免神经血管损伤并最大限度地降低再次手术的风险。准确的患者跟踪是最佳运动补偿和导航的先决条件之一。目前大多数光学跟踪系统都使用附着在脊柱上的动态参考系（DRF）来跟踪患者的运动。然而，脊柱本身会受到固有运动的影响，这可能会影响导航系统的准确性。在本研究中，我们的目的是在增强现实手术导航 (ARSN) 系统中检测光学相机捕获的不同图像视图中的实际患者脊柱特征。使用两个灰度相机采集的开放式脊柱手术病例的光学图像，在不同相机视图中识别和匹配脊柱标志。创建了一个计算机视觉框架，用于对脊柱图像进行预处理，检测和匹配局部不变图像区域。我们比较了四种特征检测算法：加速鲁棒特征 (SURF)、最大稳定极值区域 (MSER)、加速分段测试特征 (FAST) 以及定向 FAST 和旋转 Brief (ORB)，以阐明最佳方法。该框架在 23 名患者中进行了验证，匹配特征的 3D 三角测量误差<$0。5$毫米。因此，研究结果表明脊柱特征检测可用于导航手术中的准确跟踪。