Lega BC, Kramer DR, Newman JG, Lee JYK (2011) Morphometric measurements of the anterior skull base for endoscopic transoral and transnasal approaches. Skull Base 21:65–70

Article  Google Scholar 

Snyderman CH, Pant H, Carrau RL, Prevedello D, Gardner P, Kassam AB (2009) What are the limits of endoscopic sinus surgery? The expanded endonasal approach to the skull base. Keio J Med 58(3):152–160

Article  Google Scholar 

Venkatraman G, Likosky DS, Zhou W, Finlayson SRG, Goodman DC (2010) Trends in endoscopic sinus surgery rates in the Medicare population. Arch Otolaryngol Head Neck Surg 136(5):426–430

Article  Google Scholar 

Centers and P. for Disease Control (2009) National ambulatory medical care survey: 2009 summary tables. http://www.cdc.gov/nchs/data/ahcd/namcssummary/20109namcswebtables.pdf.

Pac E (2021) Position statement: intra-operative use of computer aided surgery position statement: intra-operative use of computer aided surgery. https://www.entnet.org/content/intra-operative-use-computer-aided-surgery

ARS (2019) Criteria for image guided surgery. https://www.american-rhinologic.org/index.php?option=com_content&view=article&id=35:criteria-for-image-guided-surgery&catid=26:position-statements&Itemid=197

Grimson WEL, Kikinis R, Jolesz FA, Black PM (1999) Image-guided surgery. Sci Am 280(6):62–69

CAS  Article  Google Scholar 

Bhattacharyya N, Orlandi RR, Grebner J, Martinson M (2011) Cost burden of chronic rhinosinusitis: a claims-based study. Otolaryngol Head Neck Surg 144(3):440–445

Article  Google Scholar 

Maniglia AJ (1991) Fatal and other major complications of endoscopic sinus surgery. Laryngoscope 101(4):349–354

CAS  Article  Google Scholar 

Smith TL, Mace JC, Rudmik L, Schlosser RJ, Hwang PH, Alt JA, Soler ZM (2017) Comparing surgeon outcomes in endoscopic sinus surgery for chronic rhinosinusitis. Laryngoscope 127(1):14–21

Article  Google Scholar 

Gliklich RE, Metson R (1995) The health impact of chronic sinusitis in patients seeking otolaryngologic care. Otolaryngol Head Neck Surg 113(1):104–109

CAS  Article  Google Scholar 

Soler ZM, Wittenberg E, Schlosser RJ, Mace JC, Smith TL (2011) Health state utility values in patients undergoing endoscopic sinus surgery. Laryngoscope 121(12):2672–2678

Article  Google Scholar 

Hoelzle F, Klein M, Schwerdtner O, Lueth T, Albrecht J, Hosten N, Felix R, Bier J (2001) Intraoperative computed tomography with the mobile ct Tomoscan m during surgical treatment of orbital fractures. Int J Oral Maxillofac Surg 30(1):26–31

CAS  Article  Google Scholar 

Kim TT, Drazin D, Shweikeh F, Pashman R, Johnson JP (2014) Clinical and radiographic outcomes of minimally invasive percutaneous pedicle screw placement with intraoperative ct (o-arm) image guidance navigation. Neurosurg Focus 36(3):E1

Article  Google Scholar 

Jackman AH, Palmer JN, Chiu AG, Kennedy DW (2008) Use of intraoperative ct scanning in endoscopic sinus surgery: a preliminary report. Am J Rhinol 22(2):170–174

Article  Google Scholar 

Roth M, Lanza DC, Kennedy DW, Yousem D, Scanlan KA, Zinreich J (1995) Advantages and disadvantages of three-dimensional computed tomography intraoperative localization for functional endoscopic sinus surgery. Laryngoscope 105(12):1279–1286

CAS  Article  Google Scholar 

Bolles RC, Baker HH, Marimont DH (1987) Epipolar-plane image analysis: an approach to determining structure from motion. Int J Comput Vis 1(1):7–55

Article  Google Scholar 

Collins T, Compte B, Bartoli A (2011), Deformable shape-from-motion in laparoscopy using a rigid sliding window. In: MIUA, pp 173–178

Mahmoud N, Nicolau S, Keshk A, Ahmad M, Soler L, Marescaux J (2012) Fast 3d structure from motion with missing points from registration of partial reconstructions. In: Articulated motion and deformable objects, pp 173–183

Lo B, Chung AJ, Stoyanov D, Mylonas G, Yang GZ (2008) Real-time intra-operative 3d tissue deformation recovery. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, ISBI 2008, vol 16, IEEE, Elsevier, pp 1387–1390

Röhl S, Bodenstedt S, Suwelack S, Kenngott H, Müller-Stich BP, Dillmann R, Speidel S (2012) Dense GPU-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration. Med Phys 39(3):1632–1645

Article  Google Scholar 

Horn BK, Brooks MJ (1989) Shape from shading. MIT press, New York

Google Scholar 

Prados E, Faugeras O (2006) Shape from shading. In: Handbook of mathematical models in computer vision, pp 375–388

Ciaccio EJ, Tennyson CA, Bhagat G, Lewis SK, Green PH (2013) Use of shape-from-shading to estimate three-dimensional architecture in the small intestinal lumen of celiac and control patients. Comput Methods Programs Biomed 111(3):676–684

Article  Google Scholar 

Goncalves N, Roxo D, Barreto J, Rodrigues P (2015) Perspective shape from shading for wide-FOV near-lighting endoscopes. Neurocomputing 150:136–146

Article  Google Scholar 

Karlsson N, Di Bernardo E, Ostrowski J, Goncalves L, Pirjanian P, Munich ME (2005) The VSLAM algorithm for robust localization and mapping. In: Proceedings of the 2005 IEEE international conference on robotics and automation, 2005. ICRA 2005, pp 24–29

Davison AJ, Reid ID, Molton ND, Stasse O (2007) Monoslam: real-time single camera slam. IEEE Trans Pattern Anal Mach Intell 29(6):1052–1067

Article  Google Scholar 

Mahmood F, Durr NJ (2018) Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal 48:230–243

Article  Google Scholar 

Liu X, Sinha A, Unberath M, Ishii M, Hager GD, Taylor RH, Reiter A (2018)Self-supervised learning for dense depth estimation in monocular endoscopy. In: OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis, Springer, pp 128–138

Graham B (2000) Using an accelerometer sensor to measure human hand motion. PhD thesis, Massachusetts Institute of Technology

Landau HJ (1967) Sampling, data transmission, and the Nyquist rate. In: Proceedings of the IEEE, vol 55, no 10, pp 1701–1706

M. Inc. (2021) Medtronic stealthstation series brochure. http://www.medtronic.com/us-en/healthcare-professionals/products/neurological/surgical-navigation-systems/stealthstation.html

RoCAL (2020) Medtronic stealthstation motion data read software. https://gitlab.com/RoCALab/stealth_client

Li Y, Li S, Song Q, Liu H, Meng MQ-H (2014) Fast and robust data association using posterior based approximate joint compatibility test. IEEE Trans Ind Inf 10(1):331–339

Article  Google Scholar 

Li Y, Olson EB (2010) Extracting general-purpose features from lidar data. In: 2010 IEEE International conference on robotics and automation (ICRA), IEEE, pp 1388–1393

Li Y, Hannaford B (2018) Soft-obstacle avoidance for redundant manipulators with recurrent neural network. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 1–6

Di Zenzo S (1986) A note on the gradient of a multi-image. Comput Vis Gr Image Process 33(1):116–125

Article  Google Scholar 

Rasmussen CE (2004) Gaussian processes in machine learning. Springer, Berlin, pp 63–71

Google Scholar 

Li Y, Hannaford B (2017) Gaussian process regression for sensorless grip force estimation of cable-driven elongated surgical instruments. IEEE Robot Autom Lett 2(3):1312–1319

Article  Google Scholar 

Li Y, Bly R, Harbison R, Humphreys I, Whipple M, Hannaford B, Moe K (2017) Anatomical region segmentation for objective surgical skill assessment with operating room motion data. J Neurol Surg Part B Skull Base 369:1434–1442

Google Scholar 

Raudaschl P, Zaffino P, Sharp G, Spadea M, Chen A, Dawant B, Albrecht T, Gass T, Langguth C, Luthi M, Jung F, Knapp O, Wesarg S, Haworth R, Bowes M, Ashman A, Guillard G, Brett A, Vincent G, Arteaga M, Peña D, Dominguez G, Aghdasi N, Li Y, Berens A, Moe K, Hannaford B, Schubert R, Fritscher K (2017) Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015. Med Phys 44(5):2020–2036

Article  Google Scholar