A review: artificial intelligence in image-guided spinal surgery

References

• Röntgen WC. On a new kind of rays. Science. 1896;3(59):227–231. doi: 10.1126/science.3.59.227
   PubMedGoogle Scholar
• Donald I, Macvicar J, Brown TG. Investigation of abdominal masses by pulsed ultrasound. Lancet. 1958;271(7032):1188–1195. doi: 10.1016/S0140-6736(58)91905-6
   Google Scholar
• Lauterbur PC. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature. 1973;242(5394):190–191. doi: 10.1038/242190a0
   Web of Science ®Google Scholar
• Hounsfield GN. Computerized transverse axial scanning (tomography). 1. Description of system. Br J Radiol. 1973;46(552):1016–1022. doi: 10.1259/0007-1285-46-552-1016
   PubMed Web of Science ®Google Scholar
• Cao B, Yuan B, Xu G, et al. A Pilot human cadaveric study on accuracy of the augmented reality surgical navigation system for thoracolumbar pedicle screw insertion using a new intraoperative rapid registration method. J Digit Imaging. 2023;36(4):1919–1929. doi: 10.1007/s10278-023-00840-x
   PubMedGoogle Scholar
• Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern. 1978;8(8):630–632. doi: 10.1109/TSMC.1978.4310039
   Web of Science ®Google Scholar
• Hoppe H, DeRose T, Duchamp T, et al. Surface reconstruction from unorganized points. In: Proceedings of the 19th annual conference on Computer graphics and interactive techniques; Chicago, Illinois, USA. Association for Computing Machinery; 1992. p. 71–78.
   Google Scholar
• Levoy M. Display of surfaces from volume data. IEEE Comput Graph Appl. 1988;8(3):29–37. doi: 10.1109/38.511
   Web of Science ®Google Scholar
• Meng D, Mohammed E, Boyer E, et al. (Ed.). Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle. Cham (Switzerland): Springer Nature; 2022. p. 307–317.
   Google Scholar
• Yao K, Su Z, Huang K, et al. A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE J Biomed Health Inform. 2022;26(10):4976–4986. doi: 10.1109/JBHI.2022.3162118
   PubMedGoogle Scholar
• Jiao R, Zhang Y, Ding L, et al. Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Comput Biol Med. 2024;169:107840. doi: 10.1016/j.compbiomed.2023.107840
   PubMedGoogle Scholar
• Chen Z, Tian Z, Zhu J, et al. C-CAM: causal CAM for weakly supervised semantic segmentation on medical image. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); New Orleans, USA; 2022. p. 11666–11675.
   Google Scholar
• Tang H, Chen Y, Wang T, et al. HTC-Net: a hybrid CNN-transformer framework for medical image segmentation. Biomed Signal Process Control. 2024;88:105605. doi: 10.1016/j.bspc.2023.105605
   Google Scholar
• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Boston, America. IEEE Computer Society; 2015. p. 3431–3440.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W Frangi A, editors. Medical image computing and computer-assisted intervention – MICCAI 2015. Cham: Springer International Publishing; 2015. p. 234–241.
   Google Scholar
• Xun S, Li D, Zhu H, et al. Generative adversarial networks in medical image segmentation: a review. Comput Biol Med. 2022;140:105063. doi: 10.1016/j.compbiomed.2021.105063
   PubMedGoogle Scholar
• Guinebert S, Petit E, Bousson V, et al. Automatic semantic segmentation and detection of vertebras and intervertebral discs by neural networks. Comput Methods Programs Biomed Update. 2022;2:100055. doi: 10.1016/j.cmpbup.2022.100055
   Google Scholar
• Shi W, Xu T, Yang H, et al. Attention gate based dual-pathway network for vertebra segmentation of X-Ray spine images. IEEE J Biomed Health Inform. 2022;26(8):3976–3987. doi: 10.1109/JBHI.2022.3158968
   PubMedGoogle Scholar
• Zhang L, Yang J, Liu D, et al. Spine X-ray image segmentation based on transformer and adaptive optimized postprocessing. In: 2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI); Xiamen, China; 2022. p. 88–92.
   Google Scholar
• Zhang L, Shi L, Cheng JCY, et al. LPAQR-Net: efficient vertebra segmentation from biplanar whole-spine radiographs. IEEE J Biomed Health Inform. 2021;25(7):2710–2721. doi: 10.1109/JBHI.2021.3057647
   PubMedGoogle Scholar
• You X, Gu Y, Liu Y, et al. EG-Trans3DUNet: a single-staged transformer-based model for accurate vertebrae segmentation from spinal CT images. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI); Kolkata, India; 2022. p. 1–5.
   Google Scholar
• Zhang Y, Shi Z, Wang H, et al. LumVertCancNet: a novel 3D lumbar vertebral body cancellous bone location and segmentation method based on hybrid swin-transformer. Comput Biol Med. 2024;171:108237. doi: 10.1016/j.compbiomed.2024.108237
   PubMedGoogle Scholar
• Wang Z, Zhang Z, Voiculescu I. RAR-U-NET: a residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels. In: 2021 IEEE International Conference on Image Processing (ICIP); Anchorage, Alaska, USA; 2021. p. 21–25.
   Google Scholar
• Liu X, Deng W, Liu Y. Application of hybrid network of UNet and feature pyramid network in spine segmentation. In: 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA); Neuchâtel, Switzerland; 2021. p. 1–6.
   Google Scholar
• Pang S, Pang C, Su Z, et al. DGMSNet: spine segmentation for MR image by a detection-guided mixed-supervised segmentation network. Med Image Anal. 2022;75:102261. doi: 10.1016/j.media.2021.102261
   PubMedGoogle Scholar
• Sun S, Tan ET, Mintz DN, et al. Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI. Eur Radiol. 2022;32(9):6167–6177. doi: 10.1007/s00330-022-08708-4
   PubMed Web of Science ®Google Scholar
• Bertram U, Köveshazi I, Michaelis M, et al. Man versus machine: automatic pedicle screw planning using registration-based techniques compared with manual screw planning for thoracolumbar fusion surgeries. Int J Med Robot. 1721;20(1):e2570. doi: 10.1002/rcs.2570
   Google Scholar
• Bennani H, McCane B, Cornwall J. Three-dimensional reconstruction of in vivo human lumbar spine from biplanar radiographs. Comput Med Imag Graphics. 2022;96:102011. doi: 10.1016/j.compmedimag.2021.102011
   PubMedGoogle Scholar
• Aspert N, Santa-Cruz D, Ebrahimi T. MESH: measuring errors between surfaces using the Hausdorff distance. In: Proceedings. IEEE International Conference on Multimedia and Expo; Lausanne, Switzerland; 2002. vol. 701. p. 705–708.
   Google Scholar
• Jecklin S, Jancik C, Farshad M, et al. X23D—intraoperative 3D lumbar spine shape reconstruction based on sparse multi-view X-ray data. J Imag. 2022;8(10):271. doi: 10.3390/jimaging8100271
   PubMedGoogle Scholar
• Ge R, He Y, Xia C, et al. X-CTRSNet: 3D cervical vertebra CT reconstruction and segmentation directly from 2D X-ray images. Knowl-Based Syst. 2022;236:107680. doi: 10.1016/j.knosys.2021.107680
   Google Scholar
• Liang Y, Wang C, Yu Y, et al. 3D spine model reconstruction based on RGBD images of unclothed back surface. IEEE Trans Biomed Eng. 2024;71(1):270–281. doi: 10.1109/TBME.2023.3298140
   PubMedGoogle Scholar
• Huang K, Zhang J. Three-dimensional lumbar spine generation using variational autoencoder. Med Eng Phys. 2023;120:104046. doi: 10.1016/j.medengphy.2023.104046
   PubMedGoogle Scholar
• Chen Z, Guo L, Zhang R, et al. BX2S-Net: learning to reconstruct 3D spinal structures from bi-planar X-ray images. Comput Biol Med. 2023;154:106615. doi: 10.1016/j.compbiomed.2023.106615
   PubMedGoogle Scholar
• Ou-Yang D, Burger EL, Kleck CJ. Pre-operative planning in complex deformities and use of patient-specific UNiD™ instrumentation. Global Spine J. 2022;12(2_suppl):40S–44S. doi: 10.1177/21925682211055096
   PubMedGoogle Scholar
• Goldberg JL, Härtl R, Elowitz E. Minimally invasive spine surgery: an overview. World Neurosurg. 2022;163:214–227. doi: 10.1016/j.wneu.2022.03.114
   PubMedGoogle Scholar
• Ding H, Hai Y, Zhou L, et al. Clinical application of personalized digital surgical planning and precise execution for severe and complex adult spinal deformity correction utilizing 3D printing techniques. J Pers Med. 2023;13(4):602. doi: 10.3390/jpm13040602
   PubMedGoogle Scholar
• Shi C, Sun B, Tang G, et al. Clinical and radiological outcomes of endoscopic foraminoplasty and decompression assisted with preoperative planning software for lumbar foraminal stenosis. Int J Comput Assist Radiol Surg. 2021;16(10):1829–1839. doi: 10.1007/s11548-021-02453-7
   PubMedGoogle Scholar
• Ma C, Zou D, Qi H, et al. A novel surgical planning system using an AI model to optimize planning of pedicle screw trajectories with highest bone mineral density and strongest pull-out force. J Neurosurgical Focus. 2022;52(4):E10. doi: 10.3171/2022.1.FOCUS21721
   PubMedGoogle Scholar
• Qi X, Meng J, Li M, et al. An automatic path planning method of pedicle screw placement based on preoperative CT images. IEEE Trans Med Robot Bionics. 2022;4(2):403–413. doi: 10.1109/TMRB.2022.3155288
   Google Scholar
• Qin Y, Geng P, You Y, et al. Collaborative preoperative planning for operation-navigation dual-robot orthopedic surgery system. In: IEEE Transactions on Automation Science and Engineering; 2023. p. 1–12.
   Google Scholar
• Kim I-H, Kang J, Jeong J, et al. A fully automated landmark detection for spine surgery planning with a cascaded convolutional neural net. Inf Med Unlocked. 2022;32:101045. doi: 10.1016/j.imu.2022.101045
   Google Scholar
• Schwendner M, Ille S, Wostrack M, et al. Evaluating a cutting-edge augmented reality-supported navigation system for spinal instrumentation. Eur Spine J. 2024;33(1):282–288. doi: 10.1007/s00586-023-08011-w
   PubMedGoogle Scholar
• Kudo H, Wada K, Kumagai G, et al. Accuracy of pedicle screw placement by fluoroscopy, a three-dimensional printed model, local electrical conductivity measurement device, and intraoperative computed tomography navigation in scoliosis patients. Eur J Orthop Surg Traumatol. 2021;31(3):563–569. doi: 10.1007/s00590-020-02803-2
   PubMedGoogle Scholar
• Shafi KA, Pompeu YA, Vaishnav AS, et al. Does robot-assisted navigation influence pedicle screw selection and accuracy in minimally invasive spine surgery? J Neurosurgical Focus. 2022;52(1):E4. doi: 10.3171/2021.10.FOCUS21526
   PubMedGoogle Scholar
• Han J, Luo M, You Y, et al. Real-time optimization-based tracking method for active optical navigation in orthopedic surgeries: an experimental study. IEEE Trans Med Robot Bionics. 2023;5(4):819–831. doi: 10.1109/TMRB.2023.3310018
   Google Scholar
• Huang X, Liu X, Zhu B, et al. Augmented reality surgical navigation in minimally invasive spine surgery: a preclinical study. Bioengineering. 2023;10(9):1094. doi: 10.3390/bioengineering10091094
   PubMedGoogle Scholar
• Zhu L, Shen Y, Zhao Y, et al. Surgical navigation system for spinal surgery with photoacoustic endoscopy. In: 2022 IEEE International Ultrasonics Symposium (IUS); Venice, Italy; 2022. p. 1–3.
   Google Scholar
• Liu Y, Lee M-G, Kim J-S. Spine surgery assisted by augmented reality: where have we been? Yonsei Med J. 2022;63(4):305–316. doi: 10.3349/ymj.2022.63.4.305
   PubMed Web of Science ®Google Scholar
• Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging. 1997;16(2):187–198. doi: 10.1109/42.563664
   PubMed Web of Science ®Google Scholar
• Balakrishnan G, Zhao A, Sabuncu MR, et al. VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging. 2019;38(8):1788–1800. doi: 10.1109/TMI.2019.2897538
   Web of Science ®Google Scholar
• Jiang W, Xie Q, Qin Y, et al. A novel method for spine ultrasound and X-ray radiograph registration. Ultrasonics. 2023;133:107018. doi: 10.1016/j.ultras.2023.107018
   PubMedGoogle Scholar
• Ushakiran, Naik RR, Bhat A, et al. Feature-based registration framework for pedicle screw trajectory registration between multimodal images. IEEE Access. 2023;11:59816–59826. doi: 10.1109/ACCESS.2023.3286531
   Google Scholar
• Sanhinova M, Haouchine N, Pieper SD, et al. Registration of longitudinal spine CTs for monitoring lesion growth. In: Medical Imaging. (Ed.^(Eds). 2024.
   Google Scholar
• Abumoussa A, Gopalakrishnan V, Succop B, et al. Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph. J Neurosurgical Focus. 2023;54(6):E16. doi: 10.3171/2023.3.FOCUS2345
   PubMedGoogle Scholar
• Chen M, Zhang Z, Gu S, et al. Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (Ed.^(Eds); 2024. p. 1521–1525
   Google Scholar
• Gadu SR, Potala Cs. A hybrid generative adversarial network with quantum U-NET for 3D spine X-ray image registration. Healthcare Analytics. 2023;4:100251. doi: 10.1016/j.health.2023.100251
   Google Scholar
• Liebmann F, von Atzigen M, Stütz D, et al. Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery. Med Image Anal. 2024;91:103027. doi: 10.1016/j.media.2023.103027
   PubMedGoogle Scholar
• Benzakour A, Altsitzioglou P, Lemée JM, et al. Artificial intelligence in spine surgery. Int Orthop (SICOT). 2023;47(2):457–465. doi: 10.1007/s00264-022-05517-8
   PubMed Web of Science ®Google Scholar
• Zawar A, Chhabra HS, Mundra A, et al. Robotics and navigation in spine surgery: a narrative review. J Orthop. 2023;44:36–46. doi: 10.1016/j.jor.2023.08.007
   PubMedGoogle Scholar
• Picard F, Deakin AH, Riches PE, et al. Computer assisted orthopaedic surgery: past, present and future. Med Eng Phys. 2019;72:55–65. doi: 10.1016/j.medengphy.2019.08.005
   PubMedGoogle Scholar
• Wang A, Tam TCC, Poon H, et al. NaviAirway: a bronchiole-sensitive deep learning-based airway segmentation pipeline for planning of navigation bronchoscopy. ArXiv. 2022;abs/2203.04294. https://api.semanticscholar.org/CorpusID:247319028
   Google Scholar
• Fan X, Zhu Q, Tu P, et al. A review of advances in image-guided orthopedic surgery. Phys Med Biol. 2023;68(2):02TR01. doi: 10.1088/1361-6560/acaae9
   Google Scholar
• Mangano FG, Admakin O, Lerner H, et al. Artificial intelligence and augmented reality for guided implant surgery planning: a proof of concept. J Dent. 2023;133:104485. doi: 10.1016/j.jdent.2023.104485
   PubMedGoogle Scholar
• Mohanty S, Dakua SP. Toward computing cross-modality symmetric non-rigid medical image registration. IEEE Access. 2022;10:24528–24539. doi: 10.1109/ACCESS.2022.3154771
   Google Scholar
• Wojtara M, Rana E, Rahman T, et al. Artificial intelligence in rare disease diagnosis and treatment. Clin Transl Sci. 2023;16(11):2106–2111. doi: 10.1111/cts.13619
   PubMedGoogle Scholar
• Garzia S, Capellini K, Gasparotti E, et al. Three-dimensional multi-modality registration for orthopaedics and cardiovascular settings: state-of-the-art and clinical applications. Sensors. 2024;24(4):1072. doi: 10.3390/s24041072
   PubMedGoogle Scholar