Abstract
Purpose
Surgical navigation system (SNS) has been an important tool in surgery. However, the complicated and tedious manual selection of fiducial points on preoperative images for registration affects operational efficiency to large extent. In this study, an oral and maxillofacial navigation system named BeiDou-SNS with automatic identification of fiducial points was developed and demonstrated.
Methods
To solve the fiducial selection problem, a novel method of automatic localization for titanium screw markers in preoperative images is proposed on the basis of a sequence of two local mean-shift segmentation including removal of metal artifacts. The operation of the BeiDou-SNS consists of the following key steps: The selection of fiducial points, the calibration of surgical instruments, and the registration of patient space and image space. Eight cases of patients with titanium screws as fiducial markers were carried out to analyze the accuracy of the automatic fiducial point localization algorithm. Finally, a complete phantom experiment of zygomatic implant placement surgery was performed to evaluate the whole performance of BeiDou-SNS.
Results and conclusion
The coverage of Euclidean distances between fiducial marker positions selected automatically and those selected manually by an experienced dentist for all eight cases ranged from 0.373 to 0.847 mm. Four implants were inserted into the 3D-printed model under the guide of BeiDou-SNS. And the maximal deviations between the actual and planned implant were 1.328 mm and 2.326 mm, respectively, for the entry and end point while the angular deviation ranged from 1.094° to 2.395°. The results demonstrate that the oral surgical navigation system with automatic identification of fiducial points can meet the requirements of the clinical surgeries.
Similar content being viewed by others
References
Strauss G, Koulechov K, Röttger S, Bahner J, Trantakis C, Hofer M (2006) Clinical efficiency and the influence of human factors on ear, nose, and throat navigation systems. Hno 54(12):947–957
Caversaccio M, Freysinger W (2003) Computer assistance for intraoperative navigation in ENT surgery. Minim Invasive Ther Allied Technol 12(1):36–51
Chen X, Xu L, Wang H, Wang F, Wang Q, Kikinis R (2017) Development of a surgical navigation system based on 3D Slicer for intraoperative implant placement surgery. Med Eng Phys 41:81–89
Sukegawa S, Kanno T, Furuki Y (2018) Application of computer-assisted navigation systems in oral and maxillofacial surgery. Jpn Dent Sci Rev 4(3):139–149
Brennecke T, Jansen N, Raczkowsky J, Schipper J, Woern H (2014) An ultrasound-based navigation system for minimally invasive neck surgery. Stud Health Technol Inform 196:36–42
Balachandran R, Fritz MA, Dietrich MS, Danilchenko A, Mitchell JE, Oldfield VL, Lipscomb WW, Fitzpatrick JM, Neimat JS, Konrad PE, Labadie RF (2014) Clinical testing of an alternate method of inserting bone-implanted fiducial markers. Int J Comput Assist Radiol Surg 9(5):913–920
Kobler JP, Díaz JD, Fitzpatrick JM, Lexow GJ, Majdani O, Ortmaier T (2014) Localization accuracy of sphere fiducials in computed tomography images. Proc SPIE 9036:2–7
McRackan TR, Balachandran R, Blachon GS, Mitchell JE, Noble JH, Wright CG, Fitzpatrick JM, Dawant BM, Labadie RF (2013) Validation of minimally invasive, image-guided cochlear implantation using advanced bionics, cochlear, and medel electrodes in a cadaver model. Int J Comput Assist Radiol Surg 8(6):989–995
Lin Q, Yang R, Cai K, Si X, Chen X, Wu X (2016) Real-time automatic registration in optical surgical navigation. Infrared Phys Technol 76:375–385
Zhang Y, Shen X, Hu Y (2017) Face registration and surgical instrument tracking for image-guided surgical navigation. In: International conference on virtual reality and visualization. IEEE, pp 65–71
Wang J, Suenaga H, Hoshi K, Yang L, Kobayashi E, Sakuma I, Liao H (2014) Augmented reality navigation with automatic marker-free image registration using 3-D image overlay for dental surgery. IEEE Trans Biomed Eng 61(4):1295–1304
Schwerter M, Lietzmann F, Schad LR (2016) A novel approach for a 2D/3D image registration routine for medical tool navigation in minimally invasive vascular interventions. Z Med Phys 26(3):259–269
Hong J, Hashizume M (2010) An effective point-based registration tool for surgical navigation. Surg Endosc 24(4):944–948
Peacock ZS, Magill JC, Tricomi BJ, Murphy BA, Nikonovskiy V, Hata N, Chauvin L, Troulis MJ (2015) Assessment of the osteomark-navigation system for oral and maxillofacial surgery. J Oral Maxillofac Surg 73(10):2005–2016
Chen X, Xu L, Yang Y, Egger J (2016) A semi-automatic computer-aided method for surgical template design. Sci Rep 4(6):20280
Glover GH, Pelc NJ (1981) An algorithm for the reduction of metal clip artifacts in CT reconstructions. Med Phys 8(6):799–807
Kalender WA, Hebel R, Ebersberger J (1987) Reduction of CT artifacts caused by metallic implants. Radiology 164(2):576–577
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
Deng C, Li S, Bian F, Yang Y (2015) Remote sensing image segmentation based on mean shift algorithm with adaptive bandwidth. Commun Comput Inf Sci 482:179–185
Chen X, Lin Y, Wu Y, Wang C (2008) Real-time motion tracking in image-guided oral implantology. Int J Med Robot 4(4):339–347
Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4(4):629–642
Azarmehr I, Stokbro K, Bell RB, Thygesen T (2017) Surgical navigation: a systematic review of indications, treatments, and outcomes in oral and maxillofacial surgery. J Oral Maxillofac Surg 75(9):1987–2005
West JB, Fitzpatrick JM, Toms SA, Maurer CR Jr, Maciunas RJ (2001) Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48(4):810–816
Shamir RR, Joskowicz L, Shoshan Y (2012) Fiducial optimization for minimal target registration error in image-guided neurosurgery. IEEE Trans Med Imaging 31(3):725–737
West JB, Fitzpatrick JM (2001) The distribution of target registration error in rigid-body point-based registration. IEEE Trans Med Imaging 20(9):917–927
Cai H, Xu X, Lu J, Lichtman J, Yung SP, Wong ST (2008) Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images. Med Image Anal 12(6):666–675
Acknowledgements
This work was supported by grants from National Key R&D Program of China (2017YFB1302903; 2017YFB1104100), National Natural Science Foundation of China (81828003), the Foundation of Science and Technology Commission of Shanghai Municipality (16441908400;18511108200), Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research (YG2016ZD01; YG2015MS26), and SJTU-KTH Collaborative Research and Development Seed Grants.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Rights and permissions
About this article
Cite this article
Qin, C., Cao, Z., Fan, S. et al. An oral and maxillofacial navigation system for implant placement with automatic identification of fiducial points. Int J CARS 14, 281–289 (2019). https://doi.org/10.1007/s11548-018-1870-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-018-1870-z