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http://dx.doi.org/10.7746/jkros.2020.15.4.316

AI-based Automatic Spine CT Image Segmentation and Haptic Rendering for Spinal Needle Insertion Simulator  

Park, Ikjong (POSTECH)
Kim, Keehoon (Mechanical Engineering)
Choi, Gun (Department of Spine Surgery, Woorl Spine Hospital)
Chung, Wan Kyun (POSTECH)
Publication Information
The Journal of Korea Robotics Society / v.15, no.4, 2020 , pp. 316-322 More about this Journal
Abstract
Endoscopic spine surgery is an advanced surgical technique for spinal surgery since it minimizes skin incision, muscle damage, and blood loss compared to open surgery. It requires, however, accurate positioning of an endoscope to avoid spinal nerves and to locate the endoscope near the target disk. Before the insertion of the endoscope, a guide needle is inserted to guide it. Also, the result of the surgery highly depends on the surgeons' experience and the patients' CT or MRI images. Thus, for the training, a number of haptic simulators for spinal needle insertion have been developed. But, still, it is difficult to be used in the medical field practically because previous studies require manual segmentation of vertebrae from CT images, and interaction force between the needle and soft tissue has not been considered carefully. This paper proposes AI-based automatic vertebrae CT-image segmentation and haptic rendering method using the proposed need-tissue interaction model. For the segmentation, U-net structure was implemented and the accuracy was 93% in pixel and 88% in IoU. The needle-tissue interaction model including puncture force and friction force was implemented for haptic rendering in the proposed spinal needle insertion simulator.
Keywords
Medical Robotics; Haptic Simulator; Endoscopic Spine Surgery;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015, DOI: 10.1007/978-3-319-24574-4_28.   DOI
2 A. M. Okamura, C. Simone, and Mark D. O'leary, "Force modeling for needle insertion into soft tissue," IEEE Transactions on Biomedical Engineering, vol. 51, no. 10, pp. 1707-1716, 2004, DOI: 10.1109/TBME.2004.831542.   DOI
3 J. Yao, J. E. Burns, H. Munoz, and R. M. Summers, "Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping," International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 7512, pp. 509-516, 2012, DOI: 10.1007/978-3-642-33454-2_63.   DOI
4 A. Sekuboyina, A. Bayat, M. E. Husseini, M. Loffler, M. Rempfler, J. Kukacka, G. Tetteh et al., "VerSe: A Vertebrae Labelling and Segmentation Benchmark," arXiv:2001.09193 [cs.CV], 2020, [Online], https://arxiv.org/abs/2001.09193v2.
5 C. Payer, D. Stern, H. Bischof, and M. Urschler, "Coarse to Fine Vertebrae Localization and Segmentation with Spatial-Configuration-Net and U-Net," 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, pp. 124-133, 2020, DOI: 10.5220/0008975201240133.   DOI
6 R. Assaker, R. Nicolas, P. Bruno, and P. L. Jean, "Image-guided endoscopic spine surgery: Part II: clinical applications," Spine, vol. 26, no. 15, pp. 1711-1718, 2001, [Online], https://journals.lww.com/spinejournal/Abstract/2001/08010/Image_Guided_Endoscopic_Spine_Surgery__Part_II_.16.aspx.   DOI
7 A. F. Cristante, F. Barbieri, A. A. Rodrigues da Silva, and J. C. Dellamano, "Radiation exposure during spine surgery using C-ARM fluoroscopy," Acta ortopedica brasileira, vol. 27, no. 1, pp. 46-49, 2019, DOI: 10.1590/1413-785220192701172722.   DOI
8 K. Lee, K. M. Lee, M. S. Park, B. Lee, D. G. Kwon, and C. Y. Chung, "Measurements of surgeons' exposure to ionizing radiation dose during intraoperative use of C-arm fluoroscopy," Spine, vol. 37, no. 14, pp. 1240-1244, 2012, DOI: 10.1097/BRS.0b013e31824589d5.   DOI
9 E. Archavlis, E. Schwandt, M. Kosterhon, A. Gutenberg, P. Ulrich, A. Nimer, A. Giese, and S. R. Kantelhardt, "A modified microsurgical endoscopic-assisted transpedicular corpectomy of the thoracic spine based on virtual 3-dimensional planning," World neurosurgery, vol. 91, pp. 424-433, 2016, DOI: 10.1016/j.wneu.2016.04.043.   DOI
10 Z. Hu, X. Li, J. Cui, X. He, C. Li, Y. Han, J. Pan, M. Yang, J. Tan, and L. Li, "Significance of preoperative planning software for puncture and channel establishment in percutaneous endoscopic lumbar DISCECTOMY: a study of 40 cases," International Journal of Surgery, vol. 41, pp. 97-103, 2017, DOI: 10.1016/j.ijsu.2017.03.059.   DOI
11 H. Yu, Z. Zhou, X. Lei, H. Liu, G. Fan, and S. He, "Mixed Reality-Based Preoperative Planning for Training of Percutaneous Transforaminal Endoscopic Discectomy: A Feasibility Study," World neurosurgery, vol. 129, pp. 767-775, 2019, DOI: 10.1016/j.wneu.2019.06.020.   DOI
12 P. Wei, Q. Yao, Y. Xu, H. Zhang, Y. Gu, and L. Wang, "Percutaneous kyphoplasty assisted with/without mixed reality technology in treatment of OVCF with IVC: a prospective study," Journal of orthopaedic surgery and research, vol. 14, no. 1, 2019, DOI: 10.1186/s13018-019-1303-x.   DOI
13 K. H. Fuchs, "Minimally invasive surgery," Endoscopy, vol. 34, no. 2, pp. 154-159, 2002, DOI: 10.1055/s-2002-19857.   DOI
14 G. Choi, C. S. Pophale, B. Patel, and P. Uniyal, "Endoscopic spine surgery," Journal of Korean Neurosurgical Society, vol. 60, no. 5, pp. 485-497, 2017, DOI: 10.3340/jkns.2017.0203.004.   DOI
15 P. Wucherer, P. Stefan, K. Abhari, P. Fallavollita, M. Weigl, M. Lazarovici, A. Winkler, S. Weidert, T. Peters, S. de Ribaupierre, R. Eagleson, and N. Navab, "Vertebroplasty performance on simulator for 19 surgeons using hierarchical task analysis," IEEE Transactions on Medical Imaging, vol. 34, no. 8, pp. 1730-1737, 2015, DOI: 10.1109/TMI.2015.2389033.   DOI
16 J. B. Ra, S. M. Kwon, J. K. Kim, J. Yi, K. H. Kim, H. W. Park, K.-U. Kyung, D.-S. Kwon, H. S. Kang, L. Jiang, K. R. Cleary, J. Zeng, and S. K. Min, "Visually guided spine biopsy simulator with force feedback," Medical Imaging 2001, San Diego, United States, pp. 36-45, 2001, DOI: 10.1117/12.428072.   DOI
17 M. Vania, D. Mureja, and D. Lee, "Automatic spine segmentation from CT images using convolutional neural network via redundant generation of class labels," Journal of Computational Design and Engineering, vol. 6, no. 2, pp. 224-232, 2019, DOI: 10.1016/j.jcde.2018.05.002.   DOI
18 N. Lessmann, B. van Ginneken, P. A. de Jong, and I. Isgum, "Iterative fully convolutional neural networks for automatic vertebra segmentation and identification," Medical Image Analysis, vol. 53, pp. 142-155, 2019, DOI: 10.1016/j.media.2019.02.005.   DOI