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Automatic laminectomy cutting plane planning based on artificial intelligence in robot assisted laminectomy surgery

arXiv 2312.17266 ↗First posted 26 Dec 2023eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 26 Dec 2023.

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References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  4. 2020VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Imagescited by 111
  5. 2019Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localizationcited by 77
  6. 2021A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Datacited by 28
  7. 2018Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similaritycited by 16
  8. 2018Generative Adversarial Networks for MR-CT Deformable Image Registrationcited by 12

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