Automatic laminectomy cutting plane planning based on artificial
  intelligence in robot assisted laminectomy surgery
Zhuofu Li Yonghong Zhang Chengxia Wang Shanshan Liu Xiongkang Song Xuquan Ji Shuai Jiang Woquan Zhong Lei Hu Weishi Li
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.