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3D Visualization and Augmented Reality for Orthopedics

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Intelligent Orthopaedics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1093))

Abstract

Augmented reality (AR) techniques play an important role in the field of minimally invasive surgery for orthopedics. AR can improve the hand–eye coordination by providing surgeons with the merged surgical scene, which enables surgeons to perform surgical operations more easily. To display the navigation information in the AR scene, medical image processing and three-dimensional (3D) visualization of the important anatomical structures are required. As a promising 3D display technique, integral videography (IV) can produce an autostereoscopic image with full parallax and continuous viewing points. Moreover, IV-based 3D AR navigation technique is proposed to present intuitive scene and has been applied in orthopedics, including oral surgery and spine surgery. The accurate patient-image registration, as well as the real-time target tracking for surgical tools and the patient, can be achieved. This paper overviews IV-based AR navigation and the applications in orthopedics, discusses the infrastructure required for successful implementation of IV-based approaches, and outlines the challenges that must be overcome for IV-based AR navigation to advance further development.

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References

  1. Cleary K, Peters TM (2010) Image-guided interventions: technology review and clinical applications. Annu Rev Biomed Eng 12(1):119–142

    Article  CAS  PubMed  Google Scholar 

  2. Fan Z, Weng Y, Chen G, Liao H (2017) 3D interactive surgical visualization system using mobile spatial information acquisition and autostereoscopic display. J Biomed Inform 71:154–164

    Article  PubMed  Google Scholar 

  3. Peters TM, Cleary K (2008) Image-guided interventions: technology and applications. Springer, New York (3):e50

    Google Scholar 

  4. Tang R, Ma L, Rong Z, Li M, Zeng J, Wang X, Liao H, Dong J (2017) Augmented reality technology for preoperative planning and intraoperative navigation for hepatobiliary surgery: a review of current methods. Hepatobiliary Pancreat Dis Int. https://doi.org/10.1016/S1499-3872(18)60093-1

  5. Raskar R, Welch G, Low KL, Bandyopadhyay D (2000) Shader lamps: animating real objects with image-based illumination. Eurographics: 89–102

    Google Scholar 

  6. Cakmakci O, Rolland J (2006) Head-worn displays: a review. J Disp Technol 2(3):199–216

    Article  Google Scholar 

  7. Lamata P (2010) Augmented reality for minimally invasive surgery: overview and some recent advances. Intech

    Google Scholar 

  8. Liao H, Nakajima S, Iwahara M et al (2000) Development of real-time 3D navigation system for intra-operative information by integral videography. J Japan Soc Comput Aided Surg 2(4):245–252

    Article  Google Scholar 

  9. Zhao D, Ma L, Ma C, Tang J, Liao H (2016) Floating autostereoscopic 3D display with multidimensional images for telesurgical visualization. Int J Comput Assist Radiol Surg 11(2):207–215

    Article  PubMed  Google Scholar 

  10. Tang R, Ma L, Xiang C, Wang X, Li A, Liao H, Dong J (2017) Augmented reality navigation in open surgery for hilar cholangiocarcinoma resection with hemihepatectomy using video-based in situ three-dimensional anatomical modeling: a case report. Medicine 96(37):e8083. https://doi.org/10.1097/MD.0000000000008083

    Article  PubMed  PubMed Central  Google Scholar 

  11. Oliveira FP, Tavares JM (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93

    Article  Google Scholar 

  12. Chen F, Zhao Z, Gao C, Liu J, Su X, Zhao J, Tang P, Liao H (2017) Clustering of morphological features for identifying femur cavity subtypes with difficulties of intramedullary nail implantation. IEEE J Biomed Health Inf:1. https://doi.org/10.1109/JBHI.2017.2761980

    Article  PubMed  Google Scholar 

  13. Gooya A, Liao H, Sakuma I (2012) Generalization of geometrical flux maximizing flow on riemannian manifolds for improved volumetric blood vessel segmentation. Comput Med Imaging Graph 36(6):474–483

    Article  PubMed  Google Scholar 

  14. Gooya A, Liao H, Matsumiya K, Masamune K, Masutani Y, Dohi T (2008) A variational method for geometric regularization of vascular segmentation in medical images. IEEE Trans Image Process. A Publication of the IEEE Signal Processing Society 17(8):1295–1312

    Article  Google Scholar 

  15. Shinde S, Lendal A, Bajaj N, Shelar Y (2014) Content based image retrieval and classification using support vector machine. Int J Comput Applications 92(7):8–12

    Article  Google Scholar 

  16. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(9):60

    Article  PubMed  Google Scholar 

  17. Goceri E, Goceri N (2017) Deep learning in medical image analysis: recent advances and future trends. In: The International conferences computer graphics, visualization, computer vision and image processing 2017 (CGVCVIP 2017), pp 305–311, 20–23 2017

    Google Scholar 

  18. Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth international conference on 3D vision, pp 565–571

    Google Scholar 

  19. Almeida DF, Ruben RB, Folgado J, Fernandes PR, Audenaert E, Verhegghe B, Beule M (2016) Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution ct scans. Med Eng Phys 38(12):1474–1480

    Article  PubMed  Google Scholar 

  20. Casper DS, Kim GK, Parvizi J, Freeman TA (2012) Morphology of the proximal femur differs widely with age and sex: relevance to design and selection of femoral prostheses. J Orthop Res 30(7):1162–1166

    Article  PubMed  Google Scholar 

  21. Ito M, Nakata T, Nishida A, Uetani M (2011) Age-related changes in bone density, geometry and biomechanical properties of the proximal femur: CT-based 3D hip structure analysis in normal postmenopausal women. Bone 48(3):627–630

    Article  PubMed  Google Scholar 

  22. Chen F, Liu J, Zhao Z, Zhu M, Liao H (2017) 3D feature-enhanced network for automatic femur segmentation. IEEE J Biomed Health Inform:1. https://doi.org/10.1109/JBHI.2017.2785389

  23. Lippmann G (1908) Epreuves reversibles donnant la sensation du relief. Journal De Physique Théorique Et Appliquée 7(1):821–825

    Article  Google Scholar 

  24. Liao H, Hata N, Nakajima S, Iwahara M, Sakuma I, Dohi T (2004) Surgical navigation by autostereoscopic image overlay of integral videography. IEEE Trans Inform Technol Biomed. A Publication of the IEEE Eng Med Biol Soc 8(2):114

    Article  Google Scholar 

  25. Okano F, Hoshino H, Arai J, Yuyama I (1997) Real-time pickup method for a three-dimensional image based on integral photography. Appl Opt 36(7):1598

    Article  CAS  PubMed  Google Scholar 

  26. Liao H, Nomura K, Dohi T (2006) Long visualization depth autostereoscopic display using light field rendering based integral videography. In: IEEE conference on virtual reality. vol 2006, pp 314. IEEE computer society

    Google Scholar 

  27. Liao H, Dohi T, Nomura K (2011) Autostereoscopic 3D display with long visualization depth using referential viewing area based integral photography. IEEE Trans Vis Comput Graph 17(11):1690–1701

    Article  Google Scholar 

  28. Chen G, Ma C, Fan Z, Cui X, Liao H (2017) Real-time lens based rendering algorithm for super-multiview integral photography without image resampling. IEEE Trans Vis Comput Graph:1. https://doi.org/10.1109/TVCG.2017.2756634

    Article  PubMed  Google Scholar 

  29. Wang J, Liao H (2013) Real-time 3D medical imaging using GPU-based integral videography. Med Image Tech 31:159–166

    Google Scholar 

  30. Fan Z, Chen G, Xia Y, Huang T, Liao H (2017) Accurate 3D autostereoscopic display using optimized parameters through quantitative calibration. J Opt Soc Am A Opt Image Sci Vis 34(5):804

    Article  PubMed  Google Scholar 

  31. Hutley MC, Hunt R, Stevens RF, Savander P (1994) The moire magnifier. Pure Appl Opt J Eur Opt Soc A 3(2):133

    Article  Google Scholar 

  32. Hirsch M, Lanman D, Wetzstein G, Raskar R (2013) Construction and calibration of optically efficient LCD-based multi-layer light field displays. In J Phys: Conference Series 415:012071

    Google Scholar 

  33. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36

    Article  CAS  PubMed  Google Scholar 

  34. Yaniv Z (2016) Registration for orthopaedic interventions. Computational radiology for orthopaedic interventions. Springer 23:41–70

    Google Scholar 

  35. Nogler M, Maurer H, Wimmer C, Gegenhuber C, Bach C, Krismer M (2001) Knee pain caused by a fiducial marker in the medial femoral condyle. Acta Orthop Scand 72(5):477–480

    Article  CAS  PubMed  Google Scholar 

  36. Schumann S, Nolte LP, Zheng G (2012) Determination of pelvic orientation from sparse ultrasound data for THA operated in the lateral position. Int J Med Robot Comput Assist Surg 8(1):107–113

    Article  Google Scholar 

  37. Talib H, Peterhans M, Garća J, Styner M, Ballester MAG (2011) Information filtering for ultrasound-based real-time registration. IEEE Trans Biomed Eng 58(3):531–540

    Article  PubMed  Google Scholar 

  38. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1, supplement 1):S61–S72

    Article  PubMed  Google Scholar 

  39. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Non-rigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Image 18(8):712–721

    Article  CAS  Google Scholar 

  40. Xie W, Franke J, Chen C, Grützner PA, Schumann S, Nolte L, Zheng G (2014) Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs. Int J Comput Assist Radiol Surg 9(2):165–176

    Article  PubMed  Google Scholar 

  41. Chen F, Liu J, Liao H (2016) Ultrasound navigation for transcatheter aortic stent deployment using global and local information. Appl Sci 6(12):391

    Article  Google Scholar 

  42. Chen F, Wu D, Liao H (2016) Registration of CT and ultrasound images of the spine with neural network and orientation code mutual information. In: International conference on medical imaging and virtual reality. Springer, Cham, pp 292–301

    Google Scholar 

  43. Chen F, Liu J, Liao H (2017) 3D catheter shape determination for endovascular navigation using a two-step particle filter and ultrasound scanning. IEEE Trans Med Imaging 36(3):685–695

    Article  PubMed  Google Scholar 

  44. Ma L, Zhao Z, Chen F, Zhang B, Fu L, Liao H (2017) Augmented reality surgical navigation with ultrasound-assisted registration for pedicle screw placement: a pilot study. Int J Comput Assist Radiol Surg 12(12):2205–2215

    Article  PubMed  Google Scholar 

  45. Hassfeld S, Mühling J (2001) Computer assisted oral and maxillofacial surgery--a review and an assessment of technology. Int J Oral Maxillofac Surg 30(1):2–13

    Article  CAS  PubMed  Google Scholar 

  46. Tran HH, Suenaga H, Kuwana K, Masamune K, Dohi T, Nakajima S, Liao H (2011) Augmented reality system for oral surgery using 3D auto stereoscopic visualization. In: Medical image computing and computer-assisted intervention–MICCAI 2011. Springer, Berlin pp 81–88

    Google Scholar 

  47. 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 3D image overlay for dental surgery. IEEE Trans Biomed Eng 61(4):1295–1303

    Article  PubMed  Google Scholar 

  48. Laine T, Lund T, Ylikoski M, Lohikoski J, Schlenzka D (2000) Accuracy of pedicle screw insertion with and without computer assistance: a randomised controlled clinical study in 100 consecutive patients. Eur Spine J 9(3):235–240

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  49. Manbachi A, Cobbold RS, Ginsberg HJ (2014) Guided pedicle screw insertion: techniques and training. Spine J Off J North Am Spine Soc 14(1):165–179

    Article  Google Scholar 

  50. Zhang X, Chen G, Liao H (2017) High quality see-through surgical guidance system using enhanced 3D autostereoscopic augmented reality. IEEE Trans Biomed Eng 64(8):1815–1825

    Article  PubMed  Google Scholar 

  51. Fan Z, Chen G, Wang J, Liao H (2018) A spatial position measurement system for surgical navigation using 3-D image marker-based tracking tools with compact volume. IEEE Trans Biomed Eng 65(2):378–389

    Article  PubMed  Google Scholar 

  52. Liao H, Inomata T, Sakuma I, Dohi T (2010) 3-D augmented reality for MRI-guided surgery using integral videography autostereoscopic image overlay. IEEE Trans Biomed Eng 57(6):1476–1486

    Article  PubMed  Google Scholar 

  53. Fan Z, Zhang S, Weng Y, Chen G, Liao H (2017) 3D quantitative evaluation system for autostereoscopic display. J Disp Technol 12(10):1185–1196

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge supports from National Natural Science Foundation of China (81427803, 81771940), National Key Research and Development Program of China (2017YFC0108000), Beijing Municipal Science and Technology Commission (Z151100003915079), Beijing National Science Foundation (7172122, L172003), and Soochow–Tsinghua Innovation Project (2016SZ0206).

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

    Longfei Ma, Zhencheng Fan, Guochen Ning, Xinran Zhang & Hongen Liao

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  1. Longfei Ma
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Corresponding author

Correspondence to Hongen Liao .

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Editors and Affiliations

  1. University of Bern, Bern, Switzerland

    Guoyan Zheng

  2. Beijing Jishuitan Hospital, Peking University, Beijing, Beijing, China

    Wei Tian

  3. Fudan University, Shanghai, China

    Xiahai Zhuang

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Ma, L., Fan, Z., Ning, G., Zhang, X., Liao, H. (2018). 3D Visualization and Augmented Reality for Orthopedics. In: Zheng, G., Tian, W., Zhuang, X. (eds) Intelligent Orthopaedics. Advances in Experimental Medicine and Biology, vol 1093. Springer, Singapore. https://doi.org/10.1007/978-981-13-1396-7_16

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