【摘要】 目的 基于影像引导的脊柱手术机器人系统中，2D/3D配准指的是将术前计算机断层扫描影像与术中X线片配准，用于实现手术机器人对于人体组织的精准空间定位。常见做法是先用标志点进行粗配准，再用灰度法修正位姿。标志点配准问题在于标志点识别精度不高且识别效率较低，灰度法的捕获范围小且对初始位姿敏感。由于脊柱关节边缘重叠且术中X线片图像质量较低，故利用物理边缘作为特征进行2D/3D配准精度不高。因此，提出一种基于语义边缘提取的2D/3D配准方法。方法 首先，提取X线片中成像清晰的椎弓根边缘和椎体两侧边缘作为语义特征进行2D/3D配准；同时，面向边缘提取任务，研究一种间距约束的高效“U”形变形器网络，该深度学习网络提高分割效率的同时，保持了边缘分割的准确性，并加入了椎骨间距约束损失的先验信息，进一步提升了多椎骨语义边缘提取的精度。结果 模拟数据与真实数据上评估结果表明，本文方法在配准精度与效率方面均优于现有方法；位姿修正后，本文方法平移误差小于1 mm，旋转误差小于0.1°，配准耗时在5 s左右，能较好满足实际临床需求。结论 本文提出的基于椎骨语义边缘的2D/3D粗配准方法有效缩小了后续精配准过程的搜索空间，从而提高了配准精度。在边缘提取方面，将哈达玛乘积代替卷积操作的方式以及加入椎骨间距约束损失，提高了语义边缘的提取效率和精度。因此，本文方法能够较好满足2D/3D配准的精度与实时性需求。更多还原

【Abstract】 Objective In image-guided spine surgery-based robotic systems, 2D/3D registration refers to aligning preoperative 3D computed tomography(CT) images with intraoperative 2D X-rays to achieve precise spatial localization of the surgical robot for human tissues. The prevalent approach involves the use of landmark points for initial coarse registration and subsequently applying the intensity-based method to rectify the position. However, the landmark-based registration method typically uses a heatmap regression method, which can be GPU intensive. Simultaneously, the random field of view during intraoperative X-rays and the overlapping of human tissues on X-rays can result in the loss of tissue information. This loss can cause incorrect predictions or predictions with a considerable deviation of landmark points. The intensity-based method is considered the most accurate and efficient approach because it utilizes the entire image information. However, intensitybased methods usually have problems such as a small capture range and sensitivity to the initial pose. The accuracy of 2D/3D registration when physical edges are used as features is low because of the overlap of spinal joint edges and the low quality of intraoperative radiographs. Here, a 2D/3D registration method based on semantic edge extraction is proposed.Method The semantic edge-based 2D/3D registration method uses noverlapping pedicle edges and edges on both sides of the vertebra in the X-ray as semantic features for 2D/3D registration. The real-time detection Transformer model is first used to predict the bounding boxes of the vertebrae to be registered in intraoperative X-rays. The CT images of the vertebrae to be registered are then extracted from the known vertebrae masks. The semantic edges of the vertebrae from the intraoperative X-rays and the digitally reconstructed radiological images from the CT image projection are extracted by spacing constrained and efficient UNet Transformers(SCE-UNETR). Finally, the pose is updated iteratively by minimizing the reprojection error until convergence. The SCE-UNETR network uses a U-shaped structure for encoding and decoding; it comprises an encoder module, skip connection layers, and a decoder. The SCE-UNETR encoder consists of a vision Transformer(ViT) network, where the image features are connected to the decoder by skipping connection layers. The ViT network contains a total of 12 groups of Transformer blocks, each consisting of two normalization layers, a multihead attention layer, and a multilayer perceptron. In this study, the skip connection layer and the decoder of SCE-UNETR are illuminated by a multiaxis group Hadamard product attention module instead of a convolutional operation, which changes the quadratic complexity into linear complexity and reduces the quantity of network parameters. In addition, the network incorporates the loss of the vertebral spacing constraint as a priori information, which further improves the accuracy of multivertebra semantic edge extraction.Result The method proposed in this study was quantitatively and qualitatively evaluated from different perspectives, including semantic edge extraction, coarse registration, pose refinement, method comparison, registration accuracy, and registration time. For the semantic edge extraction task, the SCE-UNETR was compared with SwinUNETR, TransUNet, and UNETR. SCE-UNETR has approximately half the number of network parameters of the other networks. However, in terms of the metrics of segmentation overlap, average Dice, average intersection over union, and average 95% Hausdorff distance, the SCE-UNETR is better than those of the other three networks. In addition, all networks show some improvement in semantic edge extraction accuracy after the spacing constraint loss term is combined with Dice loss. For the 2D/3D registration task, the experiments demonstrate that the proposed method outperforms the state-ofthe-art methods on both simulated and real data. Following pose refinement, the proposed method achieves a translation error of less than 1 mm and a rotation error of less than 0. 1°. The registration process itself takes approximately 5 s, which is well suited to the clinical environment.Conclusion The proposed semantic edge-based 2D/3D registration method effectively reduces the search space of the subsequent pose refinement process, thus improving the registration accuracy. In terms of edge extraction, the network is illuminated, and the vertebrae spacing constraint loss is added, thereby improving the efficiency and accuracy of semantic edge extraction. Therefore, the proposed method can better meet the accuracy and real-time requirements of 2D/3D registration.更多还原