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Annual Review of Biomedical Engineering

Volume 20, 2018

Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications

Abstract

Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.

Keyword(s): 3D printing, artificial intelligence, augmented reality, big data, deep learning, endoscopic vision, endoscopy, image registration, image-guided intervention, surgical navigation, surgical robotics
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/content/journals/10.1146/annurev-bioeng-062117-120917
2018-06-04
2024-04-27
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Literature Cited

  1. 1.  Marks JM, Dunkin B 2013. Principles of Flexible Endoscopy for Surgeons New York: Springer
  2. 2.  Nezhat C 2011. Nezhat's History of Endoscopy: A Historical Analysis of Endoscopy's Ascension Since Antiquity Dublin: Endo
  3. 3.  Antoniou SA, Antoniou GA, Koutras C, Antoniou AI 2012. Endoscopy and laparoscopy: a historical aspect of medical terminology. Surg. Endosc. 26:3650–54
     [Google Scholar]
  4. 4.  Roberts-Thomson IC, Singh R, Teo E, Nguyen NQ, Lidums I 2010. The future of endoscopy. J. Gastroenterol. Hepatol. 25:1051–57
     [Google Scholar]
  5. 5.  Bhatt J, Jones A, Foley S, Shah Z, Malone P et al. 2010. Harold Horace Hopkins: a short biography. BJU Int 106:1425–28
     [Google Scholar]
  6. 6.  Campbell IS, Howell JD, Evans HH 2016. Visceral vistas: Basil Hirschowitz and the birth of fiberoptic endoscopy. Ann. Intern. Med. 165:214–18
     [Google Scholar]
  7. 7.  Johansen-Berg H, Behrens TE 2009. Diffusion MRI: From Quantitative Measurement to In-Vivo Neuroanatomy Amsterdam: Elsevier
  8. 8.  Wedeen V, Wang R, Schmahmann J, Benner T, Tseng W et al. 2008. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. NeuroImage 41:1267–77
     [Google Scholar]
  9. 9.  Golby AJ 2015. Image-Guided Neurosurgery Amsterdam: Elsevier
  10. 10.  Memon A, Weber B, Winterdahl M, Jakobsen S, Meldgaard P et al. 2015. PET imaging of patients with non–small cell lung cancer employing an EGF receptor targeting drug as tracer. Br. J. Cancer 105:1850–55
     [Google Scholar]
  11. 11.  Cutsem EV, Cervantes A, Nordlinger B, Arnold D 2014. Metastatic colorectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 25:1–9
     [Google Scholar]
  12. 12.  Shaw CC 2014. Cone Beam Computed Tomography Boca Raton, FL: CRC
  13. 13.  Kapila SD 2014. Cone Beam Computed Tomography in Orthodontics: Indications, Insights, and Innovations Oxford, UK: Wiley-Blackwell
  14. 14.  Mirota DJ, Uneri A, Schafer S, Nithiananthan S, Reh DD et al. 2013. Evaluation of a system for high-accuracy 3D image–based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery. IEEE Trans. Med. Imaging 32:1215–26
     [Google Scholar]
  15. 15.  Srinivasan VM, Schafer S, Ghali MGZ, Arthur A, Duckworth EAM 2016. Cone-beam CT angiography (Dyna CT) for intraoperative localization of cerebral arteriovenous malformations. J. NeuroInterv. Surg. 8:69–74
     [Google Scholar]
  16. 16.  Errico C, Pierre J, Pezet S, Desailly Y, Lenkei Z et al. 2015. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Nature 57:499–502
     [Google Scholar]
  17. 17.  Davila JS, Momblan D, Gines A, Sanchez-Montes C, Araujo I et al. 2016. Endoscopic-assisted laparoscopic resection for gastric subepithelial tumors. Surg. Endosc. 30:199–203
     [Google Scholar]
  18. 18.  Reck M, Heigener D, Mok T, Soria J, Rabe K 2013. Management of non-small-cell lung cancer: recent developments. Lancet 382:24–30
     [Google Scholar]
  19. 19.  Sylvester PT, Evans JA, Zipfel GJ, Chole RA, Uppaluri R et al. 2015. Combined high-field intraoperative magnetic resonance imaging and endoscopy increase extent of resection and progression-free survival for pituitary adenomas. Pituitary 18:72–85
     [Google Scholar]
  20. 20.  Zaidi HA, De Los Reyes K, Barkhoudarian G, Litvack ZN, Bi WL et al. 2016. The utility of high-resolution intraoperative MRI in endoscopic transsphenoidal surgery for pituitary macroadenomas: early experience in the advanced multimodality image guided operating suite. Neurosurg. Focus 40:E18
     [Google Scholar]
  21. 21.  Zhang H, Wang F, Zhou T, Wang P, Chen X et al. 2017. Analysis of 137 patients who underwent endoscopic transsphenoidal pituitary adenoma resection under high-field intraoperative magnetic resonance imaging navigation. World Neurosurg 104:802–15
     [Google Scholar]
  22. 22.  Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG et al. 1991. Optical coherence tomography. Science 254:1178–81
     [Google Scholar]
  23. 23.  Fercher AF 1996. Optical coherence tomography. J. Biomed. Opt. 1:157–73
     [Google Scholar]
  24. 24.  Yelbuz TM, Choma MA, Thrane L, Kirby ML, Izatt JA 2002. Optical coherence tomography: a new high-resolution imaging technology to study cardiac development in chick embryos. Circulation 106:2771–74
     [Google Scholar]
  25. 25.  Jia Y, Bailey S, Wilson D, Tan O, Klein M et al. 2014. Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration. Ophthalmology 121:1435–44
     [Google Scholar]
  26. 26.  Ehlers J, Xu D, Kaiser P, Singh R, Srivastava S 2014. Intrasurgical dynamics of macular hole surgery: an assessment of surgery-induced ultrastructural alterations with intraoperative optical coherence tomography. Retina 34:213–21
     [Google Scholar]
  27. 27.  Klein BR, Brown EN, Casden RS 2016. Preoperative macular spectral-domain optical coherence tomography in patients considering advanced-technology intraocular lenses for cataract surgery. J. Cataract Refract. Surg. 42:537–41
     [Google Scholar]
  28. 28.  Drexler W, Fujimoto J 2015. Optical Coherence Tomography: Technology and Applications Berlin: Springer
  29. 29.  de Boer JF, Leitgeb R, Wojtkowski M 2017. Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT. Biomed. Opt. Express 8:3248–80
     [Google Scholar]
  30. 30.  Bailey DL, Willowson KP 2013. An evidence-based review of quantitative SPECT imaging and potential clinical applications. J. Nucl. Med. 54:83–89
     [Google Scholar]
  31. 31.  Bruyant PP 2002. Analytic and iterative reconstruction algorithms in SPECT. J. Nucl. Med. 43:1343–58
     [Google Scholar]
  32. 32.  Toney LK, Wanner M, Miyaoka RS, Alessio AM, Wood DE, Vesselle H 2014. Improved prediction of lobar perfusion contribution using technetium-99m–labeled macroaggregate of albumin single photon emission computed tomography/computed tomography with attenuation correction. J. Thorac. Cardiovasc. Surg. 148:2345–52
     [Google Scholar]
  33. 33.  Haraldsen A, Bluhme H, Røhl L, Pedersen EM, Jensen AB et al. 2016. Single photon emission computed tomography (SPECT) and SPECT/low-dose computerized tomography did not increase sensitivity or specificity compared to planar bone scintigraphy for detection of bone metastases in advanced breast cancer. Clin. Physiol. Funct. Imaging 36:40–46
     [Google Scholar]
  34. 34.  Sadowski SM, Neychev V, Millo C, Shih J, Nilubol N et al. 2016. Prospective study of 68Ga-DOTATATE positron emission tomography/computed tomography for detecting gastro-entero-pancreatic neuroendocrine tumors and unknown primary sites. J. Clin. Oncol. 34:588–96
     [Google Scholar]
  35. 35.  Winchester DE, Chauffe RJ, Meral R, Nguyen D, Ryals S et al. 2015. Clinical utility of inappropriate positron emission tomography myocardial perfusion imaging: test results and cardiovascular events. J. Nucl. Cardiol. 22:9–15
     [Google Scholar]
  36. 36.  Rochitte CE, George RT, Chen MY, Arbab-Zadeh A, Dewey M, Miller JM 2014. Computed tomography angiography and perfusion to assess coronary artery stenosis causing perfusion defects by single photon emission computed tomography: the CORE320 study. Eur. Heart J. 35:1120–30
     [Google Scholar]
  37. 37.  Greenwood JP, Herzog BA, Brown JM, Everett CC, Nixon J et al. 2016. Prognostic value of cardiovascular magnetic resonance and single-photon emission computed tomography in suspected coronary heart disease: long-term follow-up of a prospective, diagnostic accuracy cohort study. Ann. Intern. Med. 16:1–9
     [Google Scholar]
  38. 38.  Kobatake H, Masutani Y 2017. Computational Anatomy Based on Whole Body Imaging Tokyo: Springer Jpn.
  39. 39.  Bankman IN 2008. Handbook of Medical Image Processing and Analysis Amsterdam: Elsevier
  40. 40.  Smistad E, Falch TL, Bozorgi M, Elster AC, Lindseth F 2015. Medical image segmentation on GPUs—a comprehensive review. Med. Image Anal. 20:1–18
     [Google Scholar]
  41. 41.  Hajnal JV, Hill DL 2001. Medical Image Registration Boca Raton, FL: CRC
  42. 42.  Viergever MA, Maintz JA, Klein S, Murphy K, Staring M, Pluim JP 2016. A survey of medical image registration. Med. Image Anal. 33:140–44
     [Google Scholar]
  43. 43.  Schork N 2015. Personalized medicine: time for one-person trials. Nature 520:609–11
     [Google Scholar]
  44. 44.  Wu G, Shen D, Sabuncu M 2016. Machine Learning and Medical Imaging Amsterdam: Elsevier
  45. 45.  Zhou SK, Greenspan H, Shen D 2017. Deep Learning for Medical Image Analysis Amsterdam: Elsevier
  46. 46.  Deguchi D, Mori K, Feuerstein M, Kitasaka T, Mauer CR Jr. et al. 2009. Selective image similarity measure for bronchoscope tracking based on image registration. Med. Image Anal. 13:621–33
     [Google Scholar]
  47. 47.  Luo X, Feuerstein M, Deguchi D, Kitasaka T, Takabatake H, Mori K 2012. Development and comparison of new hybrid motion tracking for bronchoscopic navigation. Med. Image Anal. 16:577–96
     [Google Scholar]
  48. 48.  Merritt SA, Khare R, Bascom R, Higgins WE 2013. Interactive CT–video registration for the continuous guidance of bronchoscopy. IEEE Trans. Med. Imaging 32:1376–96
     [Google Scholar]
  49. 49.  Luo X, Mori K 2014. Discriminative structural similarity measure and its application to video-volume registration for endoscope three-dimensional motion tracking. IEEE Trans. Med. Imaging 33:1248–61
     [Google Scholar]
  50. 50.  Shen M, Giannarou S, Yang GZ 2015. Robust camera localisation with depth reconstruction for bronchoscopic navigation. Int. J. Comput. Assist. Radiol. Surg. 10:801–13
     [Google Scholar]
  51. 51.  Zhang L, Wahle A, Chen Z, Zhang L, Downe RW et al. 2015. Simultaneous registration of location and orientation in intravascular ultrasound pullbacks pairs via 3D graph–based optimization. IEEE Trans. Med. Imaging 34:2550–61
     [Google Scholar]
  52. 52.  Szeliski R 2011. Computer Vision: Algorithms and Applications London: Springer
  53. 53.  Zheng Z 2000. A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22:1330–34
     [Google Scholar]
  54. 54.  Hartley R, Kang SB 2007. Parameter-free radial distortion correction with center of distortion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 29:1309–21
     [Google Scholar]
  55. 55.  Ramalingam S, Sturm P 2017. A unifying model for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 39:1309–19
     [Google Scholar]
  56. 56.  Shiu Y, Ahmad S 1989. Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX=XB. IEEE Trans. Robot. Autom. 5:16–29
     [Google Scholar]
  57. 57.  Tsai R, Lenz R 1989. A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5:345–58
     [Google Scholar]
  58. 58.  Horaud R, Dornaika F 1995. Hand–eye calibration. Int. J. Robot. Res. 14:195–210
     [Google Scholar]
  59. 59.  Daniilidis K 1999. Hand–eye calibration using dual quaternions. Int. J. Robot. Res. 18:286–98
     [Google Scholar]
  60. 60.  Heller J, Havlena M, Sugimoto A, Pajdla T 2011. Structure-from-motion based hand–eye calibration using L∞-minimization. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition3497–503 Piscataway, NJ: IEEE
     [Google Scholar]
  61. 61.  Heller J, Havlena M, Pajdla T 2016. Globally optimal hand–eye calibration using branch-and-bound. IEEE Trans. Pattern Anal. Mach. Intell. 38:1027–33
     [Google Scholar]
  62. 62.  Wognum S, Heethuis SE, Rosario T, Hoogeman MS, Bel A 2014. Validation of deformable image registration algorithms on CT images of ex vivo porcine bladders with fiducial markers. Med. Phys. 41:071916
     [Google Scholar]
  63. 63.  Hughes-Hallett A, Mayer E, Marcus H, Cundy T, Pratt P et al. 2014. Augmented reality partial nephrectomy: examining the current status and future perspectives. Urology 83:266–73
     [Google Scholar]
  64. 64.  Inoue M, Yoshimura M, Sato S, Nakamura M, Yamada M et al. 2015. Improvement of registration accuracy in accelerated partial breast irradiation using the point-based rigid-body registration algorithm for patients with implanted fiducial markers. Med. Phys. 42:1904–10
     [Google Scholar]
  65. 65.  Tabrizi LB, Mahvash M 2015. Augmented reality–guided neurosurgery: accuracy and intraoperative application of an image projection technique. J. Neurosurg. 123:206–11
     [Google Scholar]
  66. 66.  Klein T, Traub J, Hautmann H, Ahmadian A, Navab N 2007. Fiducial-free registration procedure for navigated bronchoscopy. Lecture Notes in Computer Science, vol. 4791: Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007) N Ayache, S Ourselin, A Maeder 475–82 Heidelberg, Ger.: Springer
     [Google Scholar]
  67. 67.  Deguchi D, Feuerstein M, Kitasaka T, Suenaga Y, Ide I et al. 2012. Real-time marker-free patient registration for electromagnetic navigated bronchoscopy: a phantom study. Int. J. Comput. Assisted Radiol. Surg. 7:359–69
     [Google Scholar]
  68. 68.  Hofstad EF, Sorger H, Leira HO, Amundsen T, Lang T 2014. Automatic registration of CT images to patient during the initial phase of bronchoscopy: a clinical pilot study. Med. Phys. 41:041903
     [Google Scholar]
  69. 69.  Luo X 2014. A bronchoscopic navigation system using bronchoscope center calibration for accurate registration of electromagnetic tracker and CT volume without markers. Med. Phys. 41:061913
     [Google Scholar]
  70. 70.  Luo X, Mori K 2014. Real-time bronchoscope three-dimensional motion estimation using multiple sensor-driven alignment of CT images and electromagnetic measurements. Comput. Med. Imaging Graph. 38:540–48
     [Google Scholar]
  71. 71.  Luo X, Wan Y, He X, Mori K 2015. Adaptive marker-free registration using a multiple-point strategy for real-time and robust endoscope electromagnetic navigation. Comput. Methods Programs Biomed. 118:147–57
     [Google Scholar]
  72. 72.  Baum Z, Ungi T, Lasso A, Fichtinger G 2017. Usability of a real-time tracked augmented reality display system in musculoskeletal injections. Proc. SPIE 10135:10135T
     [Google Scholar]
  73. 73.  Holler K, Penne J, Schneider A, Jahn J, Boronat J et al. 2009. Endoscopic orientation correction. Lecture Notes in Computer Science, vol. 5761: Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009) G-Z Yang, DJ Hawkes, D Rueckert, A Noble, C Taylor 459–66 Heidelberg, Ger.: Springer
     [Google Scholar]
  74. 74.  Luo X, Kitasaka T, Mori K 2013. Externally navigated bronchoscopy using 2-D motion sensors: dynamic phantom validation. IEEE Trans. Med. Imaging 32:1725–64
     [Google Scholar]
  75. 75.  Hekimian-Williams C, Grant B, Liu X, Zhang Z, Kumar P 2010. Accurate localization of RFID tags using phase difference. Proceedings of the 2010 IEEE International Conference on RFID89–96 Piscataway, NJ: IEEE
     [Google Scholar]
  76. 76.  Wille A, Broll M, Winter S 2011. Phase difference based RFID navigation for medical applications. Proceedings of the 2011 IEEE International Conference on RFID98–105 Piscataway, NJ: IEEE
     [Google Scholar]
  77. 77.  Klayton T, Price R, Buyyounouski MK, Sobczak M, Greenberg R et al. 2012. Prostate bed motion during intensity-modulated radiotherapy treatment. Int. J. Radiat. Oncol. Biol. Phys. 84:130–36
     [Google Scholar]
  78. 78.  Curtis W, Khan M, Magnelli A, Stephans K, Tendulkar R, Xia P 2013. Relationship of imaging frequency and planning margin to account for intrafraction prostate motion: analysis based on real-time monitoring data. Int. J. Radiat. Oncol. Biol. Phys. 85:700–6
     [Google Scholar]
  79. 79.  Koizumi N, Sumiyama K, Suzuki N, Hattori A, Tajiri H, Uchiyama A 2002. Development of three-dimensional endoscopic ultrasound system with optical tracking. Lecture Notes in Computer Science, vol. 2488: Proceedings of the 3rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2002) T Dohi, R Kikinis 60–65 Heidelberg, Ger.: Springer
     [Google Scholar]
  80. 80.  Li Y, Aissaoui R, Lacoste M, Dansereau J 2004. Development and evaluation of a new body-seat interface shape measurement system. IEEE Trans. Biomed. Eng. 51:2040–50
     [Google Scholar]
  81. 81.  Housden RJ, Treece GM, Gee AH, Prager RW 2007. Hybrid systems for reconstruction of freehand 3D ultrasound data Tech. rep., Univ. Cambridge
  82. 82.  Feuerstein M, Reichl T, Vogel J, Traub J, Navab N 2009. Magneto-optical tracking of flexible laparoscopic ultrasound: model-based online detection and correction of magnetic tracking errors. IEEE Trans. Med. Imaging 28:951–67
     [Google Scholar]
  83. 83.  Soper TD, Haynor DR, Glenny RW, Seibel EJ 2010. In vivo validation of a hybrid tracking system for navigation of an ultrathin bronchoscope within peripheral airways. IEEE Trans. Biomed. Eng. 57:736–45
     [Google Scholar]
  84. 84.  Than TD, Alici G, Zhou H, Li W 2012. A review of localization systems for robotic endoscopic capsules. IEEE Trans. Biomed. Eng. 59:2387–99
     [Google Scholar]
  85. 85.  Bao G, Pahlavan K, Mi L 2015. Hybrid localization of microrobotic endoscopic capsule inside small intestine by data fusion of vision and RF sensors. IEEE Sens. J. 15:2669–78
     [Google Scholar]
  86. 86.  Reichl T, Luo X, Menzel M, Hautmann H, Mori K, Navab N 2013. Hybrid electromagnetic and image-based tracking of endoscopes with guaranteed smooth output. Int. J. Comput. Assist. Radiol. Surg. 8:955–65
     [Google Scholar]
  87. 87.  Luo X, Feuerstein M, Kitasaka T, Mori K 2012. Robust bronchoscope motion tracking using sequential Monte Carlo methods in navigated bronchoscopy: dynamic phantom and patient validation. Int. J. Comput. Assist. Radiol. Surg. 7:371–87
     [Google Scholar]
  88. 88.  Luo X, Mori K 2014. Robust endoscope motion estimation via an animated particle filter for electromagnetically navigated endoscopy. IEEE Trans. Biomed. Eng. 61:85–95
     [Google Scholar]
  89. 89.  Luo X, Jayarathne U, McLeod A, Mori K 2014. Enhanced differential evolution to combine optical mouse sensor with image structural patches for robust endoscopic navigation. Lecture Notes in Computer Science, vol. 8674: Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014) P Golland, N Hata, C Barillot, J Hornegger, R Howe 340–48 Heidelberg, Ger.: Springer
     [Google Scholar]
  90. 90.  Luo X, Wan Y, He X 2015. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion. Med. Phys. 42:1808–17
     [Google Scholar]
  91. 91.  Luo X, Wan Y, He X, Mori K 2015. Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking. Med. Image Anal. 24:282–96
     [Google Scholar]
  92. 92.  Moreland K 2013. A survey of visualization pipelines. IEEE Trans. Vis. Comput. Graph. 19:367–78
     [Google Scholar]
  93. 93.  Preim B, Bartz D 2007. Visualization in Medicine: Theory, Algorithms, and Applications San Francisco: Morgan Kaufmann
  94. 94.  Nielson G 2003. On marching cubes. IEEE Trans. Vis. Comput. Graph. 9:283–97
     [Google Scholar]
  95. 95.  Azuma R, Baillot Y, Behringer R, Feiner S, Julier S, MacIntyre B 2001. Recent advances in augmented reality. IEEE Comput. Graph. Appl. 21:34–47
     [Google Scholar]
  96. 96.  Milgram P, Kishino F 1994. A taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. 77:1321–32
     [Google Scholar]
  97. 97.  Kang X, Azizian M, Wilson E, Wu K, Martin AD et al. 2014. Stereoscopic augmented reality for laparoscopic surgery. Surg. Endosc. 28:2227–35
     [Google Scholar]
  98. 98.  Haouchine N, Cotin S, Peterlik I, Dequidt J, Lopez MS et al. 2015. Impact of soft tissue heterogeneity on augmented reality for liver surgery. IEEE Trans. Vis. Comput. Graph. 21:584–97
     [Google Scholar]
  99. 99.  Barsom EZ, Graafland M, Schijven MP 2016. Systematic review on the effectiveness of augmented reality applications in medical training. Surg. Endosc. 30:4174–83
     [Google Scholar]
  100. 100.  Bernhardt S, Nicolau SA, Soler L, Doignon C 2017. The status of augmented reality in laparoscopic surgery as of 2016. Med. Image Anal. 37:66–90
     [Google Scholar]
  101. 101.  Cleary K, Peters TM 2010. Image-guided interventions: technology review and clinical applications. Annu. Rev. Biomed. Eng. 12:119–42
     [Google Scholar]
  102. 102.  Simpson AL, Sun K, Pheiffer TS, Rucker DC, Sills AK et al. 2014. Evaluation of conoscopic holography for estimating tumor resection cavities in model-based image-guided neurosurgery. IEEE Trans. Biomed. Eng. 61:1833–43
     [Google Scholar]
  103. 103.  Torres-Corzo JG, Rangel-Castilla L, Islas-Aguilar MA, Vecchia RRD 2017. A novel approach of navigation-assisted flexible neuroendoscopy. Oper. Neurosurg. 2017:opx118
     [Google Scholar]
  104. 104.  Citardi MJ, Agbetoba A, Bigcas JL, Luong A 2016. Augmented reality for endoscopic sinus surgery with surgical navigation: a cadaver study. Int. Forum Allergy Rhinol. 6:523–28
     [Google Scholar]
  105. 105.  Chu Y, Yang J, Ai D, Li W, Song H et al. 2017. Registration and fusion quantification of augmented reality based nasal endoscopic surgery. Med. Image Anal. 42:241–56
     [Google Scholar]
  106. 106.  Gerlach T, Friebe MH 2016. Image guided laryngoscopy versus laryngectomy surgery: patient safety and system review. Cogent Eng 3:1256563
     [Google Scholar]
  107. 107.  Semmler M, Kniesburges S, Birk V, Ziethe A, Patel R, Dollinger M 2016. 3D reconstruction of human laryngeal dynamics based on endoscopic high-speed recordings. IEEE Trans. Med. Imaging 35:1615–24
     [Google Scholar]
  108. 108.  Gill R, Zheng Y, Barlow J, Jayender J, Girard E et al. 2015. Image-guided video assisted thoracoscopic surgery—phase I–II clinical trial. J. Surg. Oncol. 112:18–25
     [Google Scholar]
  109. 109.  Marchetti G, Valsecchi A, Indellicati D, Arondi S, Trigiani M, Pinelli V 2015. Ultrasound-guided medical thoracoscopy in the absence of pleural effusion. Chest 147:1008–12
     [Google Scholar]
  110. 110.  Bush C, Postma G 2013. Transnasal esophagoscopy. Otolaryngol. Clin. N. Am. 46:41–52
     [Google Scholar]
  111. 111.  Xuan Y, Hur H, Byun C, Han S, Cho Y 2013. Efficacy of intraoperative gastroscopy for tumor localization in totally laparoscopic distal gastrectomy for cancer in the middle third of the stomach. Surg. Endosc. 27:4364–70
     [Google Scholar]
  112. 112.  Li M, Zuo X, Li Y 2014. Virtual gastroscopy for the evaluation of stomach malignancy. Endoscopy 46:E320–21
     [Google Scholar]
  113. 113.  Roth HR, Hampshire TE, Helbren E, Hu M, Vega R et al. 2014. Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study. Proc. SPIE 9036:903609
     [Google Scholar]
  114. 114.  Oda M, Kondo H, Kitasaka T, Furukawa K, Miyahara R et al. 2017. Robust colonoscope tracking method for colon deformations utilizing coarse-to-fine correspondence findings. Int. J. Comput. Assist. Radiol. Surg. 12:39–50
     [Google Scholar]
  115. 115.  Robu MR, Edwards P, Ramalhinho J, Thompson S, Davidson B et al. 2017. Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery. Int. J. Comput. Assist. Radiol. Surg. 12:1079–88
     [Google Scholar]
  116. 116.  Collins JA, Weis JA, Heiselman JS, Clements LW, Simpson AL et al. 2017. Improving registration robustness for image-guided liver surgery in a novel human-to-phantom data framework. IEEE Trans. Med. Imaging 36:1502–10
     [Google Scholar]
  117. 117.  KleinJan G, van den Berg N, Brouwer O 2014. Optimisation of fluorescence guidance during robot-assisted laparoscopic sentinel node biopsy for prostate cancer. Eur. Urol. 66:991–98
     [Google Scholar]
  118. 118.  Mahara A, Khan S, Murphy EK, Schned AR, Hyams ES, Halter RJ 2015. 3D microendoscopic electrical impedance tomography for margin assessment during robot-assisted laparoscopic prostatectomy. IEEE Trans. Med. Imaging 34:1590–601
     [Google Scholar]
  119. 119.  Wallerstedt A, Tyritzis S, Thorsteinsdottir T, Carlsson S 2015. Short-term results after robot-assisted laparoscopic radical prostatectomy compared to open radical prostatectomy. Eur. Urol. 67:660–70
     [Google Scholar]
  120. 120.  Hayashi Y, Misawa K, Oda M, Hawkes DJ, Mori K 2016. Clinical application of a surgical navigation system based on virtual laparoscopy in laparoscopic gastrectomy for gastric cancer. Int. J. Comput. Assist. Radiol. Surg. 11:827–36
     [Google Scholar]
  121. 121.  Dees-Ribbers HM, Pos FJ, Betgen A, Bex A, Hulshof MC et al. 2013. Fusion of planning CT and cystoscopy images for bladder tumor delineation: a feasibility study. Med. Phys. 40:051713
     [Google Scholar]
  122. 122.  Mariappan P, Lavin V, Phua CQ, Khan SAA, Donat R, Smith G 2017. Predicting grade and stage at cystoscopy in newly presenting bladder cancers—a prospective double-blind clinical study. Urology 109:134–39
     [Google Scholar]
  123. 123.  Ghani KR, Andonian S, Bultitude M, Desai M, Giusti G et al. 2016. Percutaneous nephrolithotomy: update, trends, and future directions. Eur. Urol. 70:382–96
     [Google Scholar]
  124. 124.  Schneider A, Pezold S, Sauer A, Ebbing J, Wyler S et al. 2014. Augmented reality assisted laparoscopic partial nephrectomy. Lecture Notes in Computer Science, vol. 8674: Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014) P Golland, N Hata, C Barillot, J Hornegger, R Howe 357–64 Heidelberg, Ger.: Springer
     [Google Scholar]
  125. 125.  Kuhn AWB, Ross JR, Bedi A 2015. Three-dimensional imaging and computer navigation in planning for hip preservation surgery. Sports Med. Arthrosc. Rev. 23:e31–38
     [Google Scholar]
  126. 126.  Yan CH, Chiu KY, Ng FY, Chan PK, Fang CX 2015. Comparison between patient-specific instruments and conventional instruments and computer navigation in total knee arthroplasty: a randomized controlled trial. Knee Surg. Sports Traumatol. Arthrosc. 23:3637–45
     [Google Scholar]
  127. 127.  Ruttkay T, Gotte J, Walle U, Doll N 2015. Minimally invasive cardiac surgery using a 3D high-definition endoscopic system. Innov. Technol. Tech. Cardiothorac. Vasc. Surg. 10:431–34
     [Google Scholar]
  128. 128.  Hansen T, Majeed H 2014. Endoscopic carpal tunnel release. Hand Clin 30:47–53
     [Google Scholar]
  129. 129.  Kallewaard JW, Vanelderen P, Richardson J, Zundert JV, Heavner J, Groen GJ 2013. Epiduroscopy for patients with lumbosacral radicular pain. Pain Pract 14:365–77
     [Google Scholar]
  130. 130.  Yuan Y, Li B, Meng MQH 2015. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Trans. Autom. Sci. Eng. 13:529–35
     [Google Scholar]
  131. 131.  Karargyris A, Koulaouzidis A 2015. Odocapsule: next-generation wireless capsule endoscopy with accurate lesion localization and video stabilization capabilities. IEEE Trans. Biomed. Eng. 62:352–60
     [Google Scholar]
  132. 132.  Natali CD, Beccani M, Simaan N, Valdastri P 2016. Jacobian-based iterative method for magnetic localization in robotic capsule endoscopy. IEEE Trans. Robot. 32:327–38
     [Google Scholar]
  133. 133.  Marcus HJ, Hughes-Hallett A, Cundy TP, Yang GZ, Darzi A, Nandi D 2015. da Vinci robot-assisted keyhole neurosurgery: a cadaver study on feasibility and safety. Neurosurg. Rev. 38:367–71
     [Google Scholar]
  134. 134.  Morelli L, Di Franco G, Guadagni S, Rossi L, Palmeri M et al. 2018. Robot-assisted total mesorectal excision for rectal cancer: case-matched comparison of short-term surgical and functional outcomes between the da Vinci Xi and Si. Surg. Endosc. 32:589–600
     [Google Scholar]
  135. 135.  McGee M, Rosen M, Marks J, Onders R, Chak A et al. 2006. A primer on natural orifice transluminal endoscopic surgery: building a new paradigm.. Surg. Innov 13:86–93
     [Google Scholar]
  136. 136.  van der Stap N, Slump CH, Broeders IAMJ, van der Heijden F 2014. Image-based navigation for a robotized flexible endoscope. Lecture Notes in Computer Science, vol. 8899: Proceedings of the International Workshop on Computer-Assisted and Robotic Endoscopy (CARE 2014) X Luo, T Reichl, D Mirota, T Soper 77–87 Heidelberg, Ger.: Springer
     [Google Scholar]
  137. 137.  Pullens HJM, van der Stap N, Rozeboom ED, Schwartz MP, van der Heijden F et al. 2016. Colonoscopy with robotic steering and automated lumen centralization: a feasibility study in a colon model. Endoscopy 48:286–90
     [Google Scholar]
  138. 138.  Shi C, Luo X, Qi P, Li T, Song S et al. 2017. Shape sensing techniques for continuum robots in minimally invasive surgery: a survey. IEEE Trans. Biomed. Eng. 64:1665–78
     [Google Scholar]
  139. 139.  Konda VJ, Waxman I 2016. Endoscopic Imaging Techniques and Tools Berlin: Springer
  140. 140.  Leggett C, Gorospe E, Chan D, Muppa P, Owens V et al. 2016. Comparative diagnostic performance of volumetric laser endomicroscopy and confocal laser endomicroscopy in the detection of dysplasia associated with Barrett's esophagus. Gastrointest. Endosc. 83:880–88
     [Google Scholar]
  141. 141.  Vahrmeijer AL, Hutteman M, van der Vorst JR, van de Velde CJH, Frangioni JV 2013. Image-guided cancer surgery using near-infrared fluorescence. Nat. Rev. Clin. Oncol. 10:507–18
     [Google Scholar]
  142. 142.  McLeod A, Baxter J, de Ribaupierre S, Peters T 2014. Motion magnification for endoscopic surgery. Proc. SPIE 9036:90360C
     [Google Scholar]
  143. 143.  Luo X, McLeod A, Pautler S, Schlachta C, Peters T 2017. Vision-based surgical field defogging. IEEE Trans. Med. Imaging 36:2021–30
     [Google Scholar]
  144. 144.  Malik H, Darwood A, Shaunak S, Kulatilake P, El-Hilly A et al. 2015. Using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. J. Surg. Res. 199:512–22
     [Google Scholar]
/content/journals/10.1146/annurev-bioeng-062117-120917
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Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications
Annual Review of Biomedical Engineering 20, 221 (2018); https://doi.org/10.1146/annurev-bioeng-062117-120917
/content/journals/10.1146/annurev-bioeng-062117-120917
/content/journals/10.1146/annurev-bioeng-062117-120917
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