Objective: This study aims to use artificial intelligence to realize the automatic planning of laminectomy, and verify the method. Methods: We propose a two-stage approach for automatic laminectomy cutting plane planning. The first stage was the identification of key points. 7 key points were manually marked on each CT image. The Spatial Pyramid Upsampling Network (SPU-Net) algorithm developed by us was used to accurately locate the 7 key points. In the second stage, based on the identification of key points, a personalized coordinate system was generated for each vertebra. Finally, the transverse and longitudinal cutting planes of laminectomy were generated under the coordinate system. The overall effect of planning was evaluated. Results: In the first stage, the average localization error of the SPU-Net algorithm for the seven key points was 0.65mm. In the second stage, a total of 320 transverse cutting planes and 640 longitudinal cutting planes were planned by the algorithm. Among them, the number of horizontal plane planning effects of grade A, B, and C were 318(99.38%), 1(0.31%), and 1(0.31%), respectively. The longitudinal planning effects of grade A, B, and C were 622(97.18%), 1(0.16%), and 17(2.66%), respectively. Conclusions: In this study, we propose a method for automatic surgical path planning of laminectomy based on the localization of key points in CT images. The results showed that the method achieved satisfactory results. More studies are needed to confirm the reliability of this approach in the future.

翻译：目的：本研究旨在利用人工智能实现椎板切除术的自动规划，并对该方法进行验证。方法：我们提出了一种两阶段方法用于自动规划椎板切除切割平面。第一阶段是关键点识别。在每张CT图像上手动标记7个关键点。采用我们开发的空间金字塔上采样网络（SPU-Net）算法准确定位这7个关键点。第二阶段，基于关键点识别，为每个椎骨生成个性化坐标系。最后，在该坐标系下生成椎板切除术的横向和纵向切割平面。评估了规划的整体效果。结果：在第一阶段，SPU-Net算法对七个关键点的平均定位误差为0.65mm。在第二阶段，算法共规划了320个横向切割平面和640个纵向切割平面。其中，A级、B级和C级的水平面规划效果数量分别为318（99.38%）、1（0.31%）和1（0.31%）。A级、B级和C级的纵向规划效果数量分别为622（97.18%）、1（0.16%）和17（2.66%）。结论：在本研究中，我们提出了一种基于CT图像关键点定位的椎板切除术自动手术路径规划方法。结果表明该方法取得了满意的效果。未来需要更多研究来确认该方法的可靠性。