Augmented visualization with depth perception cues to improve the surgeon’s performance in minimally invasive surgery

  • Lucio Tommaso De PaolisEmail author
  • Valerio De Luca
Origin al Article


Minimally invasive techniques, such as laparoscopy and radiofrequency ablation of tumors, bring important advantages in surgery: by minimizing incisions on the patient’s body, they can reduce the hospitalization period and the risk of postoperative complications. Unfortunately, they come with drawbacks for surgeons, who have a restricted vision of the operation area through an indirect access and 2D images provided by a camera inserted in the body. Augmented reality provides an “X-ray vision” of the patient anatomy thanks to the visualization of the internal organs of the patient. In this way, surgeons are free from the task of mentally associating the content from CT images to the operative scene. We present a navigation system that supports surgeons in preoperative and intraoperative phases and an augmented reality system that superimposes virtual organs on the patient’s body together with depth and distance information. We implemented a combination of visual and audio cues allowing the surgeon to improve the intervention precision and avoid the risk of damaging anatomical structures. The test scenarios proved the good efficacy and accuracy of the system. Moreover, tests in the operating room suggested some modifications to the tracking system to make it more robust with respect to occlusions.

Graphical Abstract

Augmented visualization in minimally invasive surgery.


Minimally invasive surgery Augmented reality Image-guided surgery Depth perception Distance information 


  1. 1.
    Cleary K, Peters T (2010) Image-guided interventions: technology review and clinical applications. Annu Rev Biomed Eng 12:119–142PubMedGoogle Scholar
  2. 2.
    Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE Trans Med Imaging 37(8):1822–1834PubMedPubMedCentralGoogle Scholar
  3. 3.
    Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562–1573PubMedGoogle Scholar
  4. 4.
    (October, 2018) Mimics Medical Imaging Software, Materialise Group.
  5. 5.
    (October, 2018) 3D Slicer.
  6. 6.
    (October, 2018) ParaView.
  7. 7.
    Ahrens J, Geveci B, Law C (2005) 36 - ParaView: an end-user tool for large-data visualization. In: Visualization handbook. Butterworth-Heinemann, Burlington, pp 717–731Google Scholar
  8. 8.
    (October, 2018) OsiriX Imaging Software.
  9. 9.
    (October, 2018) ITK-SNAP.
  10. 10.
    Peters TM, Linte CA (2016) Image-guided interventions and computer-integrated therapy: Quo vadis? Med Image Anal 33:56–63. 20th Anniversary of the Medical Image Analysis Journal (MedIA)PubMedGoogle Scholar
  11. 11.
    Bernhardt S, Nicolau SA, Soler L, Doignon C (2017) The status of augmented reality in laparoscopic surgery as of 2016. Med Image Anal 37:66–90PubMedGoogle Scholar
  12. 12.
    Katic D, Wekerle AL, Görtler J, Spengler P, Bodenstedt S, Röhl S, Suwelack S, Kenngott HG, Wagner M, Müller-Stich BP, Dillmann R, Speidel S (2013) Context-aware augmented reality in laparoscopic surgery. Comput Med Imaging Graph 37(2):174–182. Special Issue on Mixed Reality Guidance of Therapy - Towards Clinical ImplementationPubMedGoogle Scholar
  13. 13.
    Sielhorst T, Feuerstein M, Traub J, Kutter O, Navab N (2006) CAMPAR: a software framework guaranteeing quality for medical augmented reality. Int J Comput Assist Radiol Surg 1(SUPPL. 7):29–30Google Scholar
  14. 14.
    Sauer F (2005) Image registration: enabling technology for image guided surgery and therapy. In: 2005 IEEE engineering in medicine and biology 27th annual conference, pp 7242–7245Google Scholar
  15. 15.
    Markelj P, Tomaževic D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661. Computer Assisted InterventionsPubMedGoogle Scholar
  16. 16.
    Linte CA, Camp JJ, Holmes DR, Rettmann ME, Robb RA (2013) Toward online modeling for lesion visualization and monitoring in cardiac ablation therapy. In: 16th international conference medical image computing and computer-assisted intervention – MICCAI 2013, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I. Springer Berlin Heidelberg, Berlin, pp 9–17Google Scholar
  17. 17.
    Maintz J, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36PubMedGoogle Scholar
  18. 18.
    Rolland JP, Davis L, Baillot Y (2001) A survey of tracking technology for virtual environments. Fundam Wearable Comput Augment Real 8:1–48Google Scholar
  19. 19.
    Koivukangas T, Katisko JP, Koivukangas JP (2013) Technical accuracy of optical and the electromagnetic tracking systems. SpringerPlus 2(1):1–7Google Scholar
  20. 20.
    Franz AM, Haidegger T, Birkfellner W, Cleary K, Peters TM, Maier-Hein L (2014) Electromagnetic tracking in medicine -A review of technology, validation, and applications. IEEE Trans Med Imaging 33(8):1702–1725PubMedGoogle Scholar
  21. 21.
    Su LM, Vagvolgyi BP, Agarwal R, Reiley CE, Taylor RH, Hager GD (2009) Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. Urology 73(4):896–900PubMedGoogle Scholar
  22. 22.
    Stoyanov D, Yang GZ (2009) Soft tissue deformation tracking for robotic assisted minimally invasive surgery. In: 2009 annual international conference of the IEEE engineering in medicine and biology society, pp 254–257Google Scholar
  23. 23.
    Roberts DW, Strohbehn JW, Hatch JF, Murray W, Kettenberger H (1986) A frameless stereotaxic integration of computerized tomographic imaging and the operating microscope. J Neurosurg 65(4):545–549PubMedGoogle Scholar
  24. 24.
    Kelly PJ, Kall BA, Goerss S, IV FE (1986) Computer-assisted stereotaxic laser resection of intra-axial brain neoplasms. J Neurosurg 64(3):427–439PubMedGoogle Scholar
  25. 25.
    Grimson E (1994) Automated registration for enhanced reality visualization in surgery. In: Proceedings of the 1st international symposium on medical robotics and computer assisted surgery. Pittsburg, PennsylvaniaGoogle Scholar
  26. 26.
    Watanabe E, Satoh M, Konno T, Hirai M, Yamaguchi T (2016) The trans-visible navigator: a see-through neuronavigation system using augmented reality. World Neurosurg 87:399–405PubMedGoogle Scholar
  27. 27.
    De Paolis LT, De Mauro A, Raczkowsky J, Aloisio G (2009) Virtual model of the human brain for neurosurgical simulation. In: Studies in health technology and informatics, vol 150, pp 811–815Google Scholar
  28. 28.
    Ricciardi F, Copelli C, De Paolis LT (2017) An augmented reality system for maxillo-facial surgery. Lecture notes in computer science, LNCS 10325. Springer, pp 53–62Google Scholar
  29. 29.
    Ricciardi F, Copelli C, De Paolis LT (2015) A pre-operative planning module for an augmented reality application in maxillo-facial surgery. Lecture Notes in Computer Science, LNCS 9254, Springer, pp 244–254Google Scholar
  30. 30.
    Liu L, Ecker TM, Siebenrock KA, Zheng G (2016) Computer assisted planning, simulation and navigation of periacetabular osteotomy. In: 2016 Proceedings medical imaging and augmented reality: 7th international conference, MIAR 2016. Springer International Publishing, Bern, pp 15–26Google Scholar
  31. 31.
    Lo Presti G, Freschi C, Sinceri S, Morelli G, Ferrari M, Ferrari V (2014) Virtual reality surgical navigation system for holmium laser enucleation of the prostate. In: 2014 revised selected papers augmented and virtual reality: 1st international conference, AVR 2014. Springer International Publishing , Lecce, pp 79–89Google Scholar
  32. 32.
    Wu JR, Wang ML, Liu KC, Hu MH, Lee PY (2014) Real-time advanced spinal surgery via visible patient model and augmented reality system. Comput Methods Programs Biomed 113(3):869–881PubMedGoogle Scholar
  33. 33.
    Sampogna G, Pugliese R, Elli M, Vanzulli A, Forgione A (2017) Routine clinical application of virtual reality in abdominal surgery. Minim Invasive Ther Allied Technol 26(3):1–12Google Scholar
  34. 34.
    De Paolis LT (2017) Augmented visualization as surgical support in the treatment of tumors. Lecture Notes in Computer Science, LNCS 10208. Springer, pp 432–443Google Scholar
  35. 35.
    De Paolis LT, Ricciardi F (2018) Augmented visualization in the treatment of the liver tumours with radiofrequency ablation. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization, Taylor and Francis 6(4):396–404Google Scholar
  36. 36.
    Nicolau S, Pennec X, Soler L, Buy X, Gangi A, Ayache N, Marescaux J (2009) An augmented reality system for liver thermal ablation: design and evaluation on clinical cases. Med Image Anal 13(3):494–506PubMedGoogle Scholar
  37. 37.
    De Paolis LT, Ricciardi F, Dragoni A F, Aloisio G (2011) An augmented reality application for the radio frequency ablation of the liver tumors. Lecture Notes in Computer Science, LNCS 6785 (Part 4). Springer, pp 572–581Google Scholar
  38. 38.
    Pereira PL (2007) Actual role of radiofrequency ablation of liver metastases. Eur Radiol 17(8):2062–2070PubMedGoogle Scholar
  39. 39.
    Wen R, Tay WL, Nguyen BP, Chng CB, Chui CK (2014) Hand gesture guided robot-assisted surgery based on a direct augmented reality interface. Comput Methods Prog Biomed 116(2):68–80. New methods of human-robot interaction in medical practiceGoogle Scholar
  40. 40.
    Novak EJ, Silverstein MD, Bozic KJ (2007) The cost-effectiveness of computer-assisted navigation in total knee arthroplasty. J Bone Joint Surg Am 89(11):2389–2397PubMedGoogle Scholar
  41. 41.
    De Paolis LT, Aloisio G (2010) Augmented reality in minimally invasive surgery. Lecture Notes in Electrical Engineering, LNEE 55, Springer, pp 305–320Google Scholar
  42. 42.
    Teistler M, Ampanozi G, Schweitzer W, Flach P, Thali MJ, Ebert LC (2016) Use of a low-cost three-dimensional gaming controller for forensic reconstruction of CT images. J Forensic Radiol Imaging 7:10–13Google Scholar
  43. 43.
    Jeong JW, Lee J, Park SH, Hyung WJ, Lee S (2014) Vessel navigator for surgical rehearsal system using topological map: an application to gastrectomy. In: The 2014 2nd international conference on systems and informatics (ICSAI 2014), pp 288–292Google Scholar
  44. 44.
    Turini G, Condino S, Postorino M, Ferrari V, Ferrari M (2016) Improving endovascular intraoperative navigation with real-time skeleton-based deformation of virtual vascular structures. In: 2016 Proceedings augmented reality, virtual reality, and computer graphics: 3rd international conference, AVR 2016, Part II. Springer International Publishing, Lecce, pp 82–91Google Scholar
  45. 45.
    Chen X, Xu L, Wang Y, Wang H, Wang F, Zeng X, Wang Q, Egger J (2015) Development of a surgical navigation system based on augmented reality using an optical see-through head-mounted display. J Biomed Inform 55:124–131PubMedGoogle Scholar
  46. 46.
    (October, 2018) ARToolKit.
  47. 47.
    De Paolis LT, Pulimeno M, Aloisio G (2008) An augmented reality application for minimally invasive surgery. In: IFMBE Proceedings, vol 20. Springer, pp 489–492Google Scholar
  48. 48.
    Aloisio G, Barone L, Bergamasco M, Avizzano C, De Paolis LT, Franceschini M, Mongelli A, Pantile G, Provenzano L, Raspolli M (2004) Computer-based simulator for catheter insertion training. In: Studies in health technology and informatics, vol 98, pp 4–6Google Scholar
  49. 49.
    Sánchez-Margallo FM, Sánchez-Margallo JA, Moyano-Cuevas JL, Pérez EM, Maestre J (2017) Use of natural user interfaces for image navigation during laparoscopic surgery: initial experience. Minim Invasive Ther Allied Technol 26(5):1–9Google Scholar
  50. 50.
    Santos L, Carbonaro N, Tognetti A, González JL, de la Fuente E, Fraile JC, Pérez-Turiel J (2018) Dynamic gesture recognition using a smart glove in hand-assisted laparoscopic surgery. Technologies 6 (1):8Google Scholar
  51. 51.
    De Paolis LT, Pulimeno M, Aloisio G (2010) Advanced visualization and interaction systems for surgical pre-operative planning. J Comput Inf Technol 18(4):385–392Google Scholar
  52. 52.
    Garber L (2013) Gestural technology: moving interfaces in a new direction [technology news]. Computer 46 (10):22–25Google Scholar
  53. 53.
    Invitto S, Faggiano C, Sammarco S, De Luca V, De Paolis LT (2016) Haptic, virtual interaction and motor imagery: entertainment tools and psychophysiological testing. Sensors 16(3):394Google Scholar
  54. 54.
    Zhang G, jun Zhou X, zhan Zhu C, Dong Q, Su L (2016) Usefulness of three-dimensional(3D) simulation software in hepatectomy for pediatric hepatoblastoma. Surg Oncol 25(3):236–243PubMedGoogle Scholar
  55. 55.
  56. 56.
    (October, 2018b) Vicon Bonita.
  57. 57.
    (October, 2018) MeshLab.
  58. 58.
  59. 59.
    Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4 (4):629–642Google Scholar
  60. 60.
    Sielhorst T, Bichlmeier C, Heining SM, Navab N (2006) Depth perception–a major issue in medical AR: evaluation study by twenty surgeons. Med Image Comput Comput Assist Interv 9(Pt 1):364–372PubMedGoogle Scholar
  61. 61.
    Cutting JE, Vishton PM (1995) Chapter 3 - perceiving layout and knowing distances: the integration, relative potency, and contextual use of different information about depth. In: Perception of space and motion, handbook of perception and cognition. Academic Press, San Diego, pp 69–117Google Scholar
  62. 62.
    Bichlmeier C, Navab N (2006) Virtual window for improved depth perception in medical AR. In: International workshop on augmented environments for medical imaging including augmented reality in computer-aided surgery (AMI-ARCS)Google Scholar
  63. 63.
    Bork F, Fuers B, Schneider AK, Pinto F, Graumann C, Navab N (2015) Auditory and visio-temporal distance coding for 3-dimensional perception in medical augmented reality. In: 2015 IEEE international symposium on mixed and augmented reality, pp 7–12Google Scholar
  64. 64.
    (October, 2018) PQP - A Proximity Query Package.
  65. 65.
    Larsen E, Gottschalk S, Lin MC, Manocha D (1999) Fast proximity queries with swept sphere volumes. Technical report of Department of Computer Science, UNC Chapel Hill, pp 1–32Google Scholar
  66. 66.
    (October, 2018) IGSTK - Image-Guided Surgery Toolkit.
  67. 67.
    Cleary K, Ibanez L, Ranjan S, Blake B (2004) IGSTK: a software toolkit for image-guided surgery applications. Int Congr Ser 1268(Supplement C):473–479. CARS 2004 - Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and ExhibitionGoogle Scholar
  68. 68.
    (October, 2018) ITK - Insight Segmentation and Registration Toolkit.
  69. 69.
    (October, 2018) VTK - Visualization Toolkit.
  70. 70.
    (October, 2018) FLTK - Fast Light Toolkit.
  71. 71.
    Cleary K, Cheng P, Enquobahrie A, Yaniv Z (2009) In: IGSTK: The bookGoogle Scholar
  72. 72.
    Auranuch Lorsakul CS, Jackrit S (2008) Point-cloud-to-point-cloud technique on tool calibration for dental implant surgical path trackingGoogle Scholar
  73. 73.
    (October, 2018) Blender 3D.
  74. 74.
    McGahan J, Dodd G (2001) Radiofrequency ablation of the liver: current status. Am J Roentgenol 176 (1):3–16Google Scholar
  75. 75.
    Robu MR, Edwards P, Ramalhinho J, Thompson S, Davidson B, Hawkes D, Stoyanov D, Clarkson MJ (2017) Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery. Int J Comput Assist Radiol Surg 12(7):1079–1088PubMedPubMedCentralGoogle Scholar
  76. 76.
    Scott WR, Roth G, Rivest JF (2003) View planning for automated three-dimensional object reconstruction and inspection. ACM Comput Surv 35(1):64–96Google Scholar
  77. 77.
    Sánchez-Margallo FM, Moyano-Cuevas JL, Latorre R, Maestre J, Correa L, Pagador J B, Sánchez-Peralta LF, Sánchez-Margallo JA, Usón-Gargallo J (2011) Anatomical changes due to pneumoperitoneum analyzed by mri: an experimental study in pigs. Surg Radiol Anat 33(5):389–396PubMedGoogle Scholar
  78. 78.
    Zahra Ronaghi DMK, Duffy EB (2015) Toward real-time remote processing of laparoscopic video. J Med Image 2(4):2–2–5Google Scholar
  79. 79.
    Shams R, Sadeghi P, Kennedy RA, Hartley RI (2010) A survey of medical image registration on multicore and the GPU. IEEE Signal Process Mag 27(2):50–60Google Scholar
  80. 80.
    Fluck O, Vetter C, Wein W, Kamen A, Preim B, Westermann R (2011) A survey of medical image registration on graphics hardware. Comput Methods Programs Biomed 104(3):45–57Google Scholar
  81. 81.
    Schoob A, Kundrat D, Kahrs LA, Ortmaier T (2017) Stereo vision-based tracking of soft tissue motion with application to online ablation control in laser microsurgery. Med Image Anal 40:80–95PubMedGoogle Scholar
  82. 82.
    Reichard D, Häntsch D, Bodenstedt S, Suwelack S, Wagner M, Kenngott H, Müller-Stich B, Maier-Hein L, Dillmann R, Speidel S (2017) Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery. International Journal of Computer Assisted Radiology and SurgeryGoogle Scholar
  83. 83.
    Blavier A, Gaudissart Q, Cadiere GB, Nyssen AS (2006) Impact of 2d and 3d vision on performance of novice subjects using da vinci robotic system. Acta Chir Belg 106(6):662–664PubMedGoogle Scholar
  84. 84.
    Alaraimi B, El Bakbak W, Sarker S, Makkiyah S, Al-Marzouq A, Goriparthi R, Bouhelal A, Quan V, Patel B (2014) A randomized prospective study comparing acquisition of laparoscopic skills in three-dimensional (3d) vs. two-dimensional (2d) laparoscopy. World J Surg 38(11):2746–2752PubMedGoogle Scholar
  85. 85.
    Zhang L, Zhang YQ, Zhang JS, Xu L, Jonas JB (2012) Visual fatigue and discomfort after stereoscopic display viewing. Acta Ophthalmol 91(2):e149–e153PubMedGoogle Scholar
  86. 86.
    Malik AS, Khairuddin RNHR, Amin HU, Smith ML, Kamel N, Abdullah JM, Fawzy SM, Shim S (2015) EEG based evaluation of stereoscopic 3D displays for viewer discomfort. BioMedical Engineering OnLine 14(1):21PubMedPubMedCentralGoogle Scholar
  87. 87.
    Sinha R, Raje S, Rao G (2017) Three-dimensional laparoscopy: principles and practice. J Minimal Access Surgery 13(3):165–169Google Scholar
  88. 88.
    Dixon BJ, Daly MJ, Chan H, Vescan AD, Witterick IJ, Irish JC (2013) Surgeons blinded by enhanced navigation: the effect of augmented reality on attention. Surg Endosc 27(2):454–461PubMedGoogle Scholar
  89. 89.
    Lerotic M, Chung AJ, Mylonas G, Yang GZ (2007) Pq-space based non-photorealistic rendering for augmented reality. In: 10th international conference medical image computing and computer-assisted intervention – MICCAI 2007, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part II. Springer, Berlin, pp 102–109Google Scholar
  90. 90.
    Mendez E, Kalkofen D, Schmalstieg D (2006) Interactive context-driven visualization tools for augmented reality. In: Proceedings of the 5th IEEE and ACM international symposium on mixed and augmented reality, ISMAR ’06. IEEE Computer Society, Washington, pp 209–218Google Scholar
  91. 91.
    Bichlmeier C, Wimmer F, Heining SM, Navab N (2007) Contextual anatomic mimesis hybrid in-situ visualization method for improving multi-sensory depth perception in medical augmented reality. In: 2007 6th IEEE and ACM international symposium on mixed and augmented reality, pp 129–138Google Scholar
  92. 92.
    Bichlmeier C, Heining SM, Feuerstein M, Navab N (2009) The virtual mirror: a new interaction paradigm for augmented reality environments. IEEE Trans Med Imaging 28(9):1498–1510PubMedGoogle Scholar
  93. 93.
    Reichelt S, Häussler R, Fütterer G, Leister N (2010) Depth cues in human visual perception and their realization in 3D displaysGoogle Scholar
  94. 94.
    Livatino S, De Paolis LT, D’Agostino M, Zocco A, Agrimi A, De Santis A, Bruno LV, Lapresa M (2015) Stereoscopic visualization and 3D technologies in medical endoscopic teleoperation. IEEE Trans Ind Electron 62(1):525–535Google Scholar
  95. 95.
    Nicolaou M, James A, Lo BPL, Darzi A, Yang GZ (2005) Invisible shadow for navigation and planning in minimal invasive surgery. In: 8th international conference medical image computing and computer-assisted intervention – MICCAI 2005, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II. Springer, Berlin, pp 25–32Google Scholar
  96. 96.
    Hansen C, Wieferich J, Ritter F, Rieder C, Peitgen HO (2010) Illustrative visualization of 3D planning models for augmented reality in liver surgery. Int J Comput Assist Radiol Surg 5(2):133–141PubMedGoogle Scholar
  97. 97.
    Johnson L, Edwards P, Griffin L, Hawkes D (2004) Depth perception of stereo overlays in image-guided surgeryGoogle Scholar
  98. 98.
    Kalia M, Schulte zu Berge C, Roodaki H, Chakraborty C, Navab N (2016) Interactive depth of focus for improved depth perception. In: 2016 Proceedings medical imaging and augmented reality: 7th international conference, MIAR 2016. Springer International Publishing, Bern, pp 221–232Google Scholar
  99. 99.
  100. 100.
    Mamone V, Viglialoro RM, Cutolo F, Cavallo F, Guadagni S, Ferrari V (2017) Robust laparoscopic instruments tracking using colored strips. In: 4th international conference augmented and virtual reality, and computer graphics (AVR 2017). Lecture Notes in Computer Science, LNCS 10325. Springer, Ugento, pp 129–143Google Scholar
  101. 101.
    Invitto S, Faggiano C, Sammarco S, De Luca V, De Paolis LT (2015) Interactive entertainment, virtual motion training and brain ergonomy. In: 7th international conference on intelligent technologies for interactive entertainment (INTETAIN 2015), pp 88–94Google Scholar
  102. 102.
    Lahanas V, Loukas C, Georgiou K, Lababidi H, Al-Jaroudi D (2017) Virtual reality-based assessment of basic laparoscopic skills using the leap motion controller. Surgical EndoscopyGoogle Scholar
  103. 103.
    Rawat S, Vats S, Kumar P (2016) Evaluating and exploring the MYO ARMBAND. In: 2016 international conference system modeling advancement in research trends (SMART), pp 115–120Google Scholar
  104. 104.
    Indraccolo C, De Paolis LT (2017) Augmented reality and MYO for a touchless interaction with virtual organs. Lecture notes in computer science, LNCS 10325. Springer, pp 63–73Google Scholar
  105. 105.
    De Luca V, Meo A, Mongelli A, Vecchio P, De Paolis LT (2016) Development of a virtual simulator for microanastomosis: new opportunities and challenges. Lecture notes in computer science, LNCS 9769. Springer, pp 65–81Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Engineering for InnovationUniversity of SalentoLecceItaly

Personalised recommendations